Datasets:

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[ "FDDB", "UTKFace", "SoF", "FaceForensics", "UFDD" ]

[ "AFLW", "AffectNet", "CelebA" ]

[ { "dkey": "AFLW", "dval": "The Annotated Facial Landmarks in the Wild (AFLW) is a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total about 25K faces are annotated with up to 21 landmarks per image." }, { "dkey": "AffectNet", "dval": "AffectNet is a large facial expression dataset with around 0.4 million images manually labeled for the presence of eight (neutral, happy, angry, sad, fear, surprise, disgust, contempt) facial expressions along with the intensity of valence and arousal." }, { "dkey": "CelebA", "dval": "CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age." }, { "dkey": "FDDB", "dval": "The Face Detection Dataset and Benchmark (FDDB) dataset is a collection of labeled faces from Faces in the Wild dataset. It contains a total of 5171 face annotations, where images are also of various resolution, e.g. 363x450 and 229x410. The dataset incorporates a range of challenges, including difficult pose angles, out-of-focus faces and low resolution. Both greyscale and color images are included." }, { "dkey": "UTKFace", "dval": "The UTKFace dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, age progression/regression, landmark localization, etc." }, { "dkey": "SoF", "dval": "The Specs on Faces (SoF) dataset, a collection of 42,592 (2,662×16) images for 112 persons (66 males and 46 females) who wear glasses under different illumination conditions. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is focused on two challenges: harsh illumination environments and face occlusions, which highly affect face detection, recognition, and classification. The glasses are the common natural occlusion in all images of the dataset. However, there are two more synthetic occlusions (nose and mouth) added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty (easy, medium, and hard). That enlarges the number of images to be 42,592 images (26,112 male images and 16,480 female images). There is metadata for each image that contains many information such as: the subject ID, facial landmarks, face and glasses rectangles, gender and age labels, year that the photo was taken, facial emotion, glasses type, and more." }, { "dkey": "FaceForensics", "dval": "FaceForensics is a video dataset consisting of more than 500,000 frames containing faces from 1004 videos that can be used to study image or video forgeries. All videos are downloaded from Youtube and are cut down to short continuous clips that contain mostly frontal faces. This dataset has two versions:\n\n\n\nSource-to-Target: where the authors reenact over 1000 videos with new facial expressions extracted from other videos, which e.g. can be used to train a classifier to detect fake images or videos.\n\n\n\nSelfreenactment: where the authors use Face2Face to reenact the facial expressions of videos with their own facial expressions as input to get pairs of videos, which e.g. can be used to train supervised generative refinement models." }, { "dkey": "UFDD", "dval": "Unconstrained Face Detection Dataset (UFDD) aims to fuel further research in unconstrained face detection." } ]

[ "CO-SKEL dataset", "CoSal2015", "EVALution", "MLPF" ]

[ "LSUN", "CelebA" ]

[ { "dkey": "LSUN", "dval": "The Large-scale Scene Understanding (LSUN) challenge aims to provide a different benchmark for large-scale scene classification and understanding. The LSUN classification dataset contains 10 scene categories, such as dining room, bedroom, chicken, outdoor church, and so on. For training data, each category contains a huge number of images, ranging from around 120,000 to 3,000,000. The validation data includes 300 images, and the test data has 1000 images for each category." }, { "dkey": "CelebA", "dval": "CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age." }, { "dkey": "CO-SKEL dataset", "dval": "A benchmark dataset for the co-skeletonization task." }, { "dkey": "CoSal2015", "dval": "Cosal2015 is a large-scale dataset for co-saliency detection which consists of 2,015 images of 50 categories, and each group suffers from various challenging factors such as complex environments, occlusion issues, target appearance variations and background clutters, etc. All these increase the difficulty for accurate co-saliency detection." }, { "dkey": "EVALution", "dval": "EVALution dataset is evenly distributed among the three classes (hypernyms, co-hyponyms and random) and involves three types of parts of speech (noun, verb, adjective). The full dataset contains a total of 4,263 distinct terms consisting of 2,380 nouns, 958 verbs and 972 adjectives." }, { "dkey": "MLPF", "dval": "Dataset of 50,000 top quark-antiquark (ttbar) events produced in proton-proton collisions at 14 TeV, overlaid with minimum bias events corresponding to a pileup of 200 on average. The dataset consists of detector hits as the input, generator particles as the ground truth and reconstructed particles from DELPHES for additional validation.\nThe DELPHES model corresponds to a CMS-like detector with a multi-layered charged particle tracker, an electromagnetic and hadron calorimeter. Pythia8 and Delphes3 were used for the simulation.\n\nEach file contains a bzip2-compressed python pickle with the following contents:\n```\n\n\ndata = pickle.load(bz2.BZ2File(\"out/pythia8_ttbar/tev14_pythia8_ttbar_0_0.pkl.bz2\", \"rb\"))\n\n\nEach file contains lists of arrays X (detector elements), ygen (generator particles) and ycand (rule-based PF particles from Delphes) for 100 events\n\nlen(data[\"ycand\"]), len(data[\"ygen\"]), len(data[\"X\"])\n100, 100, 100\n\n\nEach element in the list corresponds to an event. The first event in the file contains 5992 detector elements, ygen and ycand are 0-padded to the same length as X\n\ndata[\"X\"][0].shape, data[\"ygen\"][0].shape, data[\"ycand\"][0].shape, \n((5992, 12), (5992, 7), (5992, 7))\n\n\nThe X rows are detector elements: calorimeter towers and tracks with the following 12-features (0-padded)\ntower: [type==1, Et (GeV), eta, sin phi, cos phi, E (GeV), Eem (GeV), Ehad (GeV), 0, 0, 0, 0]\ntrack: [type==2, pt (GeV), eta, sin phi, cos phi, P (GeV), eta_outer, sin phi_outer, cos phi_outer, charge, is_gen_muon, is_gen_electron]\nThe ygen (ycand) rows are generator-level truth particles (rule-based PF particles from Delphes) with the following features:\n[pid, charge, pt (GeV), eta, sin phi, cos phi, E (GeV)]\npid==0: placeholder/padding entry\npid==1: charged hadrons\npid==2: neutral hadrons\npid==3: photons\npid==4: electrons\npid==5: muons\n```" } ]

[ "FineGym", "Hollywood 3D dataset", "BAR", "Image and Video Advertisements", "V-COCO" ]

[ "UCF101", "HMDB51" ]

[ { "dkey": "UCF101", "dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240." }, { "dkey": "HMDB51", "dval": "The HMDB51 dataset is a large collection of realistic videos from various sources, including movies and web videos. The dataset is composed of 6,766 video clips from 51 action categories (such as “jump”, “kiss” and “laugh”), with each category containing at least 101 clips. The original evaluation scheme uses three different training/testing splits. In each split, each action class has 70 clips for training and 30 clips for testing. The average accuracy over these three splits is used to measure the final performance." }, { "dkey": "FineGym", "dval": "FineGym is an action recognition dataset build on top of gymnasium videos. Compared to existing action recognition datasets, FineGym is distinguished in richness, quality, and diversity. In particular, it provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy. For example, a \"balance beam\" event will be annotated as a sequence of elementary sub-actions derived from five sets: \"leap-jumphop\", \"beam-turns\", \"flight-salto\", \"flight-handspring\", and \"dismount\", where the sub-action in each set will be further annotated with finely defined class labels. This new level of granularity presents significant challenges for action recognition, e.g. how to parse the temporal structures from a coherent action, and how to distinguish between subtly different action classes." }, { "dkey": "Hollywood 3D dataset", "dval": "A dataset for benchmarking action recognition algorithms in natural environments, while making use of 3D information. The dataset contains around 650 video clips, across 14 classes. In addition, two state of the art action recognition algorithms are extended to make use of the 3D data, and five new interest point detection strategies are also proposed, that extend to the 3D data." }, { "dkey": "BAR", "dval": "Biased Action Recognition (BAR) dataset is a real-world image dataset categorized as six action classes which are biased to distinct places. The authors settle these six action classes by inspecting imSitu, which provides still action images from Google Image Search with action and place labels. In detail, the authors choose action classes where images for each of these candidate actions share common place characteristics. At the same time, the place characteristics of action class candidates should be distinct in order to classify the action only from place attributes. The select pairs are six typical action-place pairs: (Climbing, RockWall), (Diving, Underwater), (Fishing, WaterSurface), (Racing, APavedTrack), (Throwing, PlayingField),and (Vaulting, Sky)." }, { "dkey": "Image and Video Advertisements", "dval": "The Image and Video Advertisements collection consists of an image dataset of 64,832 image ads, and a video dataset of 3,477 ads. The data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer (\"What should I do according to this ad, and why should I do it? \"), and symbolic references ads make (e.g. a dove symbolizes peace)." }, { "dkey": "V-COCO", "dval": "Verbs in COCO (V-COCO) is a dataset that builds off COCO for human-object interaction detection. V-COCO provides 10,346 images (2,533 for training, 2,867 for validating and 4,946 for testing) and 16,199 person instances. Each person has annotations for 29 action categories and there are no interaction labels including objects." } ]

[ "TDIUC", "iVQA", "VQA-E", "TextVQA", "CommonsenseQA" ]

[ "ImageNet", "DBpedia", "COCO" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "DBpedia", "dval": "DBpedia (from \"DB\" for \"database\") is a project aiming to extract structured content from the information created in the Wikipedia project. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets." }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "TDIUC", "dval": "Task Directed Image Understanding Challenge (TDIUC) dataset is a Visual Question Answering dataset which consists of 1.6M questions and 170K images sourced from MS COCO and the Visual Genome Dataset. The image-question pairs are split into 12 categories and 4 additional evaluation matrices which help evaluate models’ robustness against answer imbalance and its ability to answer questions that require higher reasoning capability. The TDIUC dataset divides the VQA paradigm into 12 different task directed question types. These include questions that require a simpler task (e.g., object presence, color attribute) and more complex tasks (e.g., counting, positional reasoning). The dataset includes also an “Absurd” question category in which questions are irrelevant to the image contents to help balance the dataset." }, { "dkey": "iVQA", "dval": "An open-ended VideoQA benchmark that aims to: i) provide a well-defined evaluation by including five correct answer annotations per question and ii) avoid questions which can be answered without the video. \n\niVQA contains 10,000 video clips with one question and five corresponding answers per clip. Moreover, we manually reduce the language bias by excluding questions that could be answered without watching the video." }, { "dkey": "VQA-E", "dval": "VQA-E is a dataset for Visual Question Answering with Explanation, where the models are required to generate and explanation with the predicted answer. The VQA-E dataset is automatically derived from the VQA v2 dataset by synthesizing a textual explanation for each image-question-answer triple." }, { "dkey": "TextVQA", "dval": "TextVQA is a dataset to benchmark visual reasoning based on text in images.\nTextVQA requires models to read and reason about text in images to answer questions about them. Specifically, models need to incorporate a new modality of text present in the images and reason over it to answer TextVQA questions.\n\nStatistics\n* 28,408 images from OpenImages\n* 45,336 questions\n* 453,360 ground truth answers" }, { "dkey": "CommonsenseQA", "dval": "The CommonsenseQA is a dataset for commonsense question answering task. The dataset consists of 12,247 questions with 5 choices each.\nThe dataset was generated by Amazon Mechanical Turk workers in the following process (an example is provided in parentheses):\n\n\na crowd worker observes a source concept from ConceptNet (“River”) and three target concepts (“Waterfall”, “Bridge”, “Valley”) that are all related by the same ConceptNet relation (“AtLocation”),\nthe worker authors three questions, one per target concept, such that only that particular target concept is the answer, while the other two distractor concepts are not, (“Where on a river can you hold a cup upright to catch water on a sunny day?”, “Where can I stand on a river to see water falling without getting wet?”, “I’m crossing the river, my feet are wet but my body is dry, where am I?”)\nfor each question, another worker chooses one additional distractor from Concept Net (“pebble”, “stream”, “bank”), and the author another distractor (“mountain”, “bottom”, “island”) manually." } ]

[ "E2E", "Glint360K", "HellaSwag", "RFW", "OGB-LSC" ]

[ "ImageNet", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "E2E", "dval": "End-to-End NLG Challenge (E2E) aims to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena." }, { "dkey": "Glint360K", "dval": "The largest and cleanest face recognition dataset Glint360K, \nwhich contains 17,091,657 images of 360,232 individuals, baseline models trained on Glint360K can easily achieve state-of-the-art performance." }, { "dkey": "HellaSwag", "dval": "HellaSwag is a challenge dataset for evaluating commonsense NLI that is specially hard for state-of-the-art models, though its questions are trivial for humans (>95% accuracy)." }, { "dkey": "RFW", "dval": "To validate the racial bias of four commercial APIs and four state-of-the-art (SOTA) algorithms." }, { "dkey": "OGB-LSC", "dval": "OGB Large-Scale Challenge (OGB-LSC) is a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification. \n\nOGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task.\n\n\nMAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification).\nWikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction).\nPCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression)." } ]

[ "SNIPS", "Multi-Modal CelebA-HQ", "C&Z", "ConvAI2", "iFakeFaceDB", "Dayton" ]

[ "CIFAR-10", "CelebA" ]

[ { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "CelebA", "dval": "CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "Multi-Modal CelebA-HQ", "dval": "Multi-Modal-CelebA-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has high-quality segmentation mask, sketch, descriptive text, and image with transparent background.\n\nMulti-Modal-CelebA-HQ can be used to train and evaluate algorithms of text-to-image-generation, text-guided image manipulation, sketch-to-image generation, and GANs for face generation and editing." }, { "dkey": "C&Z", "dval": "One of the first datasets (if not the first) to highlight the importance of bias and diversity in the community, which started a revolution afterwards. Introduced in 2014 as integral part of a thesis of Master of Science [1,2] at Carnegie Mellon and City University of Hong Kong. It was later expanded by adding synthetic images generated by a GAN architecture at ETH Zürich (in HDCGAN by Curtó et al. 2017). Being then not only the pioneer of talking about the importance of balanced datasets for learning and vision but also for being the first GAN augmented dataset of faces. \n\nThe original description goes as follows:\n\nA bias-free dataset, containing human faces from different ethnical groups in a wide variety of illumination conditions and image resolutions. C&Z is enhanced with HDCGAN synthetic images, thus being the first GAN augmented dataset of faces.\n\nDataset: https://github.com/curto2/c\n\nSupplement (with scripts to handle the labels): https://github.com/curto2/graphics\n\n[1] https://www.curto.hk/c/decurto.pdf\n\n[2] https://www.zarza.hk/z/dezarza.pdf" }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "iFakeFaceDB", "dval": "iFakeFaceDB is a face image dataset for the study of synthetic face manipulation detection, comprising about 87,000 synthetic face images generated by the Style-GAN model and transformed with the GANprintR approach. All images were aligned and resized to the size of 224 x 224." }, { "dkey": "Dayton", "dval": "The Dayton dataset is a dataset for ground-to-aerial (or aerial-to-ground) image translation, or cross-view image synthesis. It contains images of road views and aerial views of roads. There are 76,048 images in total and the train/test split is 55,000/21,048. The images in the original dataset have 354×354 resolution." } ]

[ "QNLI", "OLPBENCH", "PAQ", "VQA-E", "MATINF", "VCR" ]

[ "RACE", "MultiNLI", "MovieQA", "SQuAD" ]

[ { "dkey": "RACE", "dval": "The ReAding Comprehension dataset from Examinations (RACE) dataset is a machine reading comprehension dataset consisting of 27,933 passages and 97,867 questions from English exams, targeting Chinese students aged 12-18. RACE consists of two subsets, RACE-M and RACE-H, from middle school and high school exams, respectively. RACE-M has 28,293 questions and RACE-H has 69,574. Each question is associated with 4 candidate answers, one of which is correct. The data generation process of RACE differs from most machine reading comprehension datasets - instead of generating questions and answers by heuristics or crowd-sourcing, questions in RACE are specifically designed for testing human reading skills, and are created by domain experts." }, { "dkey": "MultiNLI", "dval": "The Multi-Genre Natural Language Inference (MultiNLI) dataset has 433K sentence pairs. Its size and mode of collection are modeled closely like SNLI. MultiNLI offers ten distinct genres (Face-to-face, Telephone, 9/11, Travel, Letters, Oxford University Press, Slate, Verbatim, Goverment and Fiction) of written and spoken English data. There are matched dev/test sets which are derived from the same sources as those in the training set, and mismatched sets which do not closely resemble any seen at training time." }, { "dkey": "MovieQA", "dval": "The MovieQA dataset is a dataset for movie question answering. to evaluate automatic story comprehension from both video and text. The data set consists of almost 15,000 multiple choice question answers obtained from over 400 movies and features high semantic diversity. Each question comes with a set of five highly plausible answers; only one of which is correct. The questions can be answered using multiple sources of information: movie clips, plots, subtitles, and for a subset scripts and DVS." }, { "dkey": "SQuAD", "dval": "The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text. Because the questions and answers are produced by humans through crowdsourcing, it is more diverse than some other question-answering datasets. SQuAD 1.1 contains 107,785 question-answer pairs on 536 articles. SQuAD2.0 (open-domain SQuAD, SQuAD-Open), the latest version, combines the 100,000 questions in SQuAD1.1 with over 50,000 un-answerable questions written adversarially by crowdworkers in forms that are similar to the answerable ones." }, { "dkey": "QNLI", "dval": "The QNLI (Question-answering NLI) dataset is a Natural Language Inference dataset automatically derived from the Stanford Question Answering Dataset v1.1 (SQuAD). SQuAD v1.1 consists of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The dataset was converted into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue. The QNLI dataset is part of GLEU benchmark." }, { "dkey": "OLPBENCH", "dval": "OLPBENCH is a large Open Link Prediction benchmark, which was derived from the state-of-the-art Open Information Extraction corpus OPIEC (Gashteovski et al., 2019). OLPBENCH contains 30M open triples, 1M distinct open relations and 2.5M distinct mentions of approximately 800K entities. \n\nOpen Link Prediction is defined as follows: Given an Open Knowledge Graph and a question consisting of an entity mention and an open relation, predict mentions as answers. A predicted mention is correct if it is a mention of the correct answer entity. For example, given the question (“NBC-TV”, “has office in”, ?), correct answers include “NYC” and “New York”." }, { "dkey": "PAQ", "dval": "Probably Asked Questions (PAQ) is a very large resource of 65M automatically-generated QA-pairs. PAQ is a semi-structured Knowledge Base (KB) of 65M natural language QA-pairs, which models can memorise and/or learn to retrieve from. PAQ differs from traditional KBs in that questions and answers are stored in natural language, and that questions are generated such that they are likely to appear in ODQA datasets. PAQ is automatically constructed using a question generation model and Wikipedia." }, { "dkey": "VQA-E", "dval": "VQA-E is a dataset for Visual Question Answering with Explanation, where the models are required to generate and explanation with the predicted answer. The VQA-E dataset is automatically derived from the VQA v2 dataset by synthesizing a textual explanation for each image-question-answer triple." }, { "dkey": "MATINF", "dval": "Maternal and Infant (MATINF) Dataset is a large-scale dataset jointly labeled for classification, question answering and summarization in the domain of maternity and baby caring in Chinese. An entry in the dataset includes four fields: question (Q), description (D), class (C) and answer (A).\n\nNearly two million question-answer pairs are collected with fine-grained human-labeled classes from a large Chinese maternity and baby caring QA site. Authors conduct both automatic and manual data cleansing and remove: (1) classes with insufficient samples; (2) entries in which the length of the description filed is less than the length of the question field; (3) data with any field longer than 256 characters; (4) human-spotted ill-formed data. After the data cleansing, MATINF is constructed with the remaining 1.07 million entries" }, { "dkey": "VCR", "dval": "Visual Commonsense Reasoning (VCR) is a large-scale dataset for cognition-level visual understanding. Given a challenging question about an image, machines need to present two sub-tasks: answer correctly and provide a rationale justifying its answer. The VCR dataset contains over 212K (training), 26K (validation) and 25K (testing) questions, answers and rationales derived from 110K movie scenes." } ]

[ "AFEW-VA", "iSUN", "MPIIGaze", "ETH-XGaze", "Raindrop" ]

[ "SALICON", "LSMDC" ]

[ { "dkey": "SALICON", "dval": "The SALIency in CONtext (SALICON) dataset contains 10,000 training images, 5,000 validation images and 5,000 test images for saliency prediction. This dataset has been created by annotating saliency in images from MS COCO.\nThe ground-truth saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded.\nThe training and validation sets, provided with ground truth, contain the following data fields: image, resolution and gaze.\nThe testing data contains only the image and resolution fields." }, { "dkey": "LSMDC", "dval": "This dataset contains 118,081 short video clips extracted from 202 movies. Each video has a caption, either extracted from the movie script or from transcribed DVS (descriptive video services) for the visually impaired. The validation set contains 7408 clips and evaluation is performed on a test set of 1000 videos from movies disjoint from the training and val sets." }, { "dkey": "AFEW-VA", "dval": "The AFEW-VA databaset is a collection of highly accurate per-frame annotations levels of valence and arousal, along with per-frame annotations of 68 facial landmarks for 600 challenging video clips. These clips are extracted from feature films and were also annotated in terms of discrete emotion categories in the form of the AFEW database (that can be obtained there)." }, { "dkey": "iSUN", "dval": "iSUN is a ground truth of gaze traces on images from the SUN dataset. The collection is partitioned into 6,000 images for training, 926 for validation and 2,000 for test." }, { "dkey": "MPIIGaze", "dval": "MPIIGaze is a dataset for appearance-based gaze estimation in the wild. It contains 213,659 images collected from 15 participants during natural everyday laptop use over more than three months. It has a large variability in appearance and illumination." }, { "dkey": "ETH-XGaze", "dval": "Consists of over one million high-resolution images of varying gaze under extreme head poses. The dataset is collected from 110 participants with a custom hardware setup including 18 digital SLR cameras and adjustable illumination conditions, and a calibrated system to record ground truth gaze targets. \n\nOfficial competition on Codalab: https://competitions.codalab.org/competitions/28930" }, { "dkey": "Raindrop", "dval": "Raindrop is a set of image pairs, where\neach pair contains exactly the same background scene, yet\none is degraded by raindrops and the other one is free from\nraindrops. To obtain this, the images are captured through two pieces of exactly the\nsame glass: one sprayed with water, and the other is left\nclean. The dataset consists of 1,119 pairs of images, with various\nbackground scenes and raindrops. They were captured with a Sony A6000\nand a Canon EOS 60." } ]

[ "SHREC", "3D Hand Pose", "StanfordExtra", "LOCATA" ]

[ "ITOP", "K2HPD" ]

[ { "dkey": "ITOP", "dval": "The ITOP dataset consists of 40K training and 10K testing depth images for each of the front-view and top-view tracks. This dataset contains depth images with 20 actors who perform 15 sequences each and is recorded by two Asus Xtion Pro cameras. The ground-truth of this dataset is the 3D coordinates of 15 body joints." }, { "dkey": "K2HPD", "dval": "Includes 100K depth images under challenging scenarios." }, { "dkey": "SHREC", "dval": "The SHREC dataset contains 14 dynamic gestures performed by 28 participants (all participants are right handed) and captured by the Intel RealSense short range depth camera. Each gesture is performed between 1 and 10 times by each participant in two way: using one finger and the whole hand. Therefore, the dataset is composed by 2800 sequences captured. The depth image, with a resolution of 640x480, and the coordinates of 22 joints (both in the 2D depth image space and in the 3D world space) are saved for each frame of each sequence in the dataset." }, { "dkey": "3D Hand Pose", "dval": "3D Hand Pose is a multi-view hand pose dataset consisting of color images of hands and different kind of annotations for each: the bounding box and the 2D and 3D location on the joints in the hand." }, { "dkey": "StanfordExtra", "dval": "An 'in the wild' dataset of 20,580 dog images for which 2D joint and silhouette annotations were collected." }, { "dkey": "LOCATA", "dval": "The LOCATA dataset is a dataset for acoustic source localization. It consists of real-world ambisonic speech recordings with optically tracked azimuth-elevation labels." } ]

[ "InteriorNet", "GoPro", "Fraunhofer IPA Bin-Picking", "3D-FRONT" ]

[ "KITTI", "Argoverse" ]

[ { "dkey": "KITTI", "dval": "KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist." }, { "dkey": "Argoverse", "dval": "Argoverse is a tracking benchmark with over 30K scenarios collected in Pittsburgh and Miami. Each scenario is a sequence of frames sampled at 10 HZ. Each sequence has an interesting object called “agent”, and the task is to predict the future locations of agents in a 3 seconds future horizon. The sequences are split into training, validation and test sets, which have 205,942, 39,472 and 78,143 sequences respectively. These splits have no geographical overlap." }, { "dkey": "InteriorNet", "dval": "InteriorNet is a RGB-D for large scale interior scene understanding and mapping. The dataset contains 20M images created by pipeline:\n\n\n(A) the authors collected around 1 million CAD models provided by world-leading furniture manufacturers.\n(B) based on those models, around 1,100 professional designers create around 22 million interior layouts. Most of such layouts have been used in real-world decorations.\n(C) For each layout, authors generate a number of configurations to represent different random lightings and simulation of scene change over time in daily life.\n(D) Authors provide an interactive simulator (ViSim) to help for creating ground truth IMU, events, as well as monocular or stereo camera trajectories including hand-drawn, random walking and neural network based realistic trajectory.\n(E) All supported image sequences and ground truth." }, { "dkey": "GoPro", "dval": "The GoPro dataset for deblurring consists of 3,214 blurred images with the size of 1,280×720 that are divided into 2,103 training images and 1,111 test images. The dataset consists of pairs of a realistic blurry image and the corresponding ground truth shapr image that are obtained by a high-speed camera." }, { "dkey": "Fraunhofer IPA Bin-Picking", "dval": "The Fraunhofer IPA Bin-Picking dataset is a large-scale dataset comprising both simulated and real-world scenes for various objects (potentially having symmetries) and is fully annotated with 6D poses. A pyhsics simulation is used to create scenes of many parts in bulk by dropping objects in a random position and orientation above a bin. Additionally, this dataset extends the Siléane dataset by providing more samples. This allows to e.g. train deep neural networks and benchmark the performance on the public Siléane dataset" }, { "dkey": "3D-FRONT", "dval": "3D-FRONT (3D Furnished Rooms with layOuts and semaNTics) is large-scale, and comprehensive repository of synthetic indoor scenes highlighted by professionally designed layouts and a large number of rooms populated by high-quality textured 3D models with style compatibility. From layout semantics down to texture details of individual objects, the dataset is freely available to the academic community and beyond. \n\n3D-FRONT contains 18,797 rooms diversely furnished by 3D objects. In addition, the 7,302 furniture objects all come with high-quality textures. While the floorplans and layout designs are directly sourced from professional creations, the interior designs in terms of furniture styles, color, and textures have been carefully curated based on a recommender system to attain consistent styles as expert designs." } ]

[ "BAR", "Image and Video Advertisements", "CUB-200-2011", "SNIPS", "Kinetics-600", "UCF101", "GYAFC" ]

[ "ImageNet", "Flickr30k" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "Flickr30k", "dval": "The Flickr30k dataset contains 31,000 images collected from Flickr, together with 5 reference sentences provided by human annotators." }, { "dkey": "BAR", "dval": "Biased Action Recognition (BAR) dataset is a real-world image dataset categorized as six action classes which are biased to distinct places. The authors settle these six action classes by inspecting imSitu, which provides still action images from Google Image Search with action and place labels. In detail, the authors choose action classes where images for each of these candidate actions share common place characteristics. At the same time, the place characteristics of action class candidates should be distinct in order to classify the action only from place attributes. The select pairs are six typical action-place pairs: (Climbing, RockWall), (Diving, Underwater), (Fishing, WaterSurface), (Racing, APavedTrack), (Throwing, PlayingField),and (Vaulting, Sky)." }, { "dkey": "Image and Video Advertisements", "dval": "The Image and Video Advertisements collection consists of an image dataset of 64,832 image ads, and a video dataset of 3,477 ads. The data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer (\"What should I do according to this ad, and why should I do it? \"), and symbolic references ads make (e.g. a dove symbolizes peace)." }, { "dkey": "CUB-200-2011", "dval": "The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "Kinetics-600", "dval": "The Kinetics-600 is a large-scale action recognition dataset which consists of around 480K videos from 600 action categories. The 480K videos are divided into 390K, 30K, 60K for training, validation and test sets, respectively. Each video in the dataset is a 10-second clip of action moment annotated from raw YouTube video. It is an extensions of the Kinetics-400 dataset." }, { "dkey": "UCF101", "dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240." }, { "dkey": "GYAFC", "dval": "Grammarly’s Yahoo Answers Formality Corpus (GYAFC) is the largest dataset for any style containing a total of 110K informal / formal sentence pairs.\n\nYahoo Answers is a question answering forum, contains a large number of informal sentences and allows redistribution of data. The authors used the Yahoo Answers L6 corpus to create the GYAFC dataset of informal and formal sentence pairs. In order to ensure a uniform distribution of data, they removed sentences that are questions, contain URLs, and are shorter than 5 words or longer than 25. After these preprocessing steps, 40 million sentences remain. \n\nThe Yahoo Answers corpus consists of several different domains like Business, Entertainment & Music, Travel, Food, etc. Pavlick and Tetreault formality classifier (PT16) shows that the formality level varies significantly\nacross different genres. In order to control for this variation, the authors work with two specific domains that contain the most informal sentences and show results on training and testing within those categories. The authors use the formality classifier from PT16 to identify informal sentences and train this classifier on the Answers genre of the PT16 corpus\nwhich consists of nearly 5,000 randomly selected sentences from Yahoo Answers manually annotated on a scale of -3 (very informal) to 3 (very formal). They find that the domains of Entertainment & Music and Family & Relationships contain the most informal sentences and create the GYAFC dataset using these domains." } ]

[ "Syn2Real", "Dialogue State Tracking Challenge", "EPIC-KITCHENS-100", "Libri-Adapt" ]

[ "GTA5", "Cityscapes" ]

[ { "dkey": "GTA5", "dval": "The GTA5 dataset contains 24966 synthetic images with pixel level semantic annotation. The images have been rendered using the open-world video game Grand Theft Auto 5 and are all from the car perspective in the streets of American-style virtual cities. There are 19 semantic classes which are compatible with the ones of Cityscapes dataset." }, { "dkey": "Cityscapes", "dval": "Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. Data was captured in 50 cities during several months, daytimes, and good weather conditions. It was originally recorded as video so the frames were manually selected to have the following features: large number of dynamic objects, varying scene layout, and varying background." }, { "dkey": "Syn2Real", "dval": "Syn2Real, a synthetic-to-real visual domain adaptation benchmark meant to encourage further development of robust domain transfer methods. The goal is to train a model on a synthetic \"source\" domain and then update it so that its performance improves on a real \"target\" domain, without using any target annotations. It includes three tasks, illustrated in figures above: the more traditional closed-set classification task with a known set of categories; the less studied open-set classification task with unknown object categories in the target domain; and the object detection task, which involves localizing instances of objects by predicting their bounding boxes and corresponding class labels." }, { "dkey": "Dialogue State Tracking Challenge", "dval": "The Dialog State Tracking Challenges 2 & 3 (DSTC2&3) were research challenge focused on improving the state of the art in tracking the state of spoken dialog systems. State tracking, sometimes called belief tracking, refers to accurately estimating the user's goal as a dialog progresses. Accurate state tracking is desirable because it provides robustness to errors in speech recognition, and helps reduce ambiguity inherent in language within a temporal process like dialog.\nIn these challenges, participants were given labelled corpora of dialogs to develop state tracking algorithms. The trackers were then evaluated on a common set of held-out dialogs, which were released, un-labelled, during a one week period.\n\nThe corpus was collected using Amazon Mechanical Turk, and consists of dialogs in two domains: restaurant information, and tourist information. Tourist information subsumes restaurant information, and includes bars, cafés etc. as well as multiple new slots. There were two rounds of evaluation using this data:\n\nDSTC 2 released a large number of training dialogs related to restaurant search. Compared to DSTC (which was in the bus timetables domain), DSTC 2 introduces changing user goals, tracking 'requested slots' as well as the new restaurants domain. Results from DSTC 2 were presented at SIGDIAL 2014.\nDSTC 3 addressed the problem of adaption to a new domain - tourist information. DSTC 3 releases a small amount of labelled data in the tourist information domain; participants will use this data plus the restaurant data from DSTC 2 for training.\nDialogs used for training are fully labelled; user transcriptions, user dialog-act semantics and dialog state are all annotated. (This corpus therefore is also suitable for studies in Spoken Language Understanding.)" }, { "dkey": "EPIC-KITCHENS-100", "dval": "This paper introduces the pipeline to scale the largest dataset in egocentric vision EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (EPIC-KITCHENS-55), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection also enables evaluating the \"test of time\" - i.e. whether models trained on data collected in 2018 can generalise to new footage collected under the same hypotheses albeit \"two years on\".\nThe dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics." }, { "dkey": "Libri-Adapt", "dval": "Libri-Adapt aims to support unsupervised domain adaptation research on speech recognition models." } ]

[ "300-VW", "ConvAI2", "AffectNet", "SoF", "LaPa" ]

[ "AFW", "AFLW", "300W" ]

[ { "dkey": "AFW", "dval": "AFW (Annotated Faces in the Wild) is a face detection dataset that contains 205 images with 468 faces. Each face image is labeled with at most 6 landmarks with visibility labels, as well as a bounding box." }, { "dkey": "AFLW", "dval": "The Annotated Facial Landmarks in the Wild (AFLW) is a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total about 25K faces are annotated with up to 21 landmarks per image." }, { "dkey": "300W", "dval": "The 300-W is a face dataset that consists of 300 Indoor and 300 Outdoor in-the-wild images. It covers a large variation of identity, expression, illumination conditions, pose, occlusion and face size. The images were downloaded from google.com by making queries such as “party”, “conference”, “protests”, “football” and “celebrities”. Compared to the rest of in-the-wild datasets, the 300-W database contains a larger percentage of partially-occluded images and covers more expressions than the common “neutral” or “smile”, such as “surprise” or “scream”.\nImages were annotated with the 68-point mark-up using a semi-automatic methodology. The images of the database were carefully selected so that they represent a characteristic sample of challenging but natural face instances under totally unconstrained conditions. Thus, methods that achieve accurate performance on the 300-W database can demonstrate the same accuracy in most realistic cases.\nMany images of the database contain more than one annotated faces (293 images with 1 face, 53 images with 2 faces and 53 images with [3, 7] faces). Consequently, the database consists of 600 annotated face instances, but 399 unique images. Finally, there is a large variety of face sizes. Specifically, 49.3% of the faces have size in the range [48.6k, 2.0M] and the overall mean size is 85k (about 292 × 292) pixels." }, { "dkey": "300-VW", "dval": "300 Videos in the Wild (300-VW) is a dataset for evaluating facial landmark tracking algorithms in the wild. The dataset authors collected a large number of long facial videos recorded in the wild. Each video has duration of ~1 minute (at 25-30 fps). All frames have been annotated with regards to the same mark-up (i.e. set of facial landmarks) used in the 300 W competition as well (a total of 68 landmarks). The dataset includes 114 videos (circa 1 min each)." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "AffectNet", "dval": "AffectNet is a large facial expression dataset with around 0.4 million images manually labeled for the presence of eight (neutral, happy, angry, sad, fear, surprise, disgust, contempt) facial expressions along with the intensity of valence and arousal." }, { "dkey": "SoF", "dval": "The Specs on Faces (SoF) dataset, a collection of 42,592 (2,662×16) images for 112 persons (66 males and 46 females) who wear glasses under different illumination conditions. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is focused on two challenges: harsh illumination environments and face occlusions, which highly affect face detection, recognition, and classification. The glasses are the common natural occlusion in all images of the dataset. However, there are two more synthetic occlusions (nose and mouth) added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty (easy, medium, and hard). That enlarges the number of images to be 42,592 images (26,112 male images and 16,480 female images). There is metadata for each image that contains many information such as: the subject ID, facial landmarks, face and glasses rectangles, gender and age labels, year that the photo was taken, facial emotion, glasses type, and more." }, { "dkey": "LaPa", "dval": "A large-scale Landmark guided face Parsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks." } ]

[ "Raindrop", "Dayton", "FDF", "ISTD", "WinoGrande", "Multi-Modal CelebA-HQ" ]

[ "ImageNet", "COCO" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "Raindrop", "dval": "Raindrop is a set of image pairs, where\neach pair contains exactly the same background scene, yet\none is degraded by raindrops and the other one is free from\nraindrops. To obtain this, the images are captured through two pieces of exactly the\nsame glass: one sprayed with water, and the other is left\nclean. The dataset consists of 1,119 pairs of images, with various\nbackground scenes and raindrops. They were captured with a Sony A6000\nand a Canon EOS 60." }, { "dkey": "Dayton", "dval": "The Dayton dataset is a dataset for ground-to-aerial (or aerial-to-ground) image translation, or cross-view image synthesis. It contains images of road views and aerial views of roads. There are 76,048 images in total and the train/test split is 55,000/21,048. The images in the original dataset have 354×354 resolution." }, { "dkey": "FDF", "dval": "A diverse dataset of human faces, including unconventional poses, occluded faces, and a vast variability in backgrounds." }, { "dkey": "ISTD", "dval": "The Image Shadow Triplets dataset (ISTD) is a dataset for shadow understanding that contains 1870 image triplets of shadow image, shadow mask, and shadow-free image." }, { "dkey": "WinoGrande", "dval": "WinoGrande is a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations." }, { "dkey": "Multi-Modal CelebA-HQ", "dval": "Multi-Modal-CelebA-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has high-quality segmentation mask, sketch, descriptive text, and image with transparent background.\n\nMulti-Modal-CelebA-HQ can be used to train and evaluate algorithms of text-to-image-generation, text-guided image manipulation, sketch-to-image generation, and GANs for face generation and editing." } ]

[ "EPIC-KITCHENS-100", "SUN397", "TAPOS", "LRS3-TED", "V-COCO", "Kinetics", "PA-HMDB51" ]

[ "UCF101", "HMDB51" ]

[ { "dkey": "UCF101", "dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240." }, { "dkey": "HMDB51", "dval": "The HMDB51 dataset is a large collection of realistic videos from various sources, including movies and web videos. The dataset is composed of 6,766 video clips from 51 action categories (such as “jump”, “kiss” and “laugh”), with each category containing at least 101 clips. The original evaluation scheme uses three different training/testing splits. In each split, each action class has 70 clips for training and 30 clips for testing. The average accuracy over these three splits is used to measure the final performance." }, { "dkey": "EPIC-KITCHENS-100", "dval": "This paper introduces the pipeline to scale the largest dataset in egocentric vision EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (EPIC-KITCHENS-55), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection also enables evaluating the \"test of time\" - i.e. whether models trained on data collected in 2018 can generalise to new footage collected under the same hypotheses albeit \"two years on\".\nThe dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics." }, { "dkey": "SUN397", "dval": "The Scene UNderstanding (SUN) database contains 899 categories and 130,519 images. There are 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition." }, { "dkey": "TAPOS", "dval": "TAPOS is a new dataset developed on sport videos with manual annotations of sub-actions, and conduct a study on temporal action parsing on top. A sport activity usually consists of multiple sub-actions and that the awareness of such temporal structures is beneficial to action recognition.\n\nTAPOS contains 16,294 valid instances in total, across 21 action classes. These instances have a duration of 9.4\nseconds on average. The number of instances within each class is different, where the largest class high jump has over\n1,600 instances, and the smallest class beam has 200 instances. The average number of sub-actions also varies\nfrom class to class, where parallel bars has 9 sub-actions on average, and long jump has 3 sub-actions on average. All instances are split into train, validation and test sets, of sizes 13094, 1790, and 1763, respectively." }, { "dkey": "LRS3-TED", "dval": "LRS3-TED is a multi-modal dataset for visual and audio-visual speech recognition. It includes face tracks from over 400 hours of TED and TEDx videos, along with the corresponding subtitles and word alignment boundaries. The new dataset is substantially larger in scale compared to other public datasets that are available for general research." }, { "dkey": "V-COCO", "dval": "Verbs in COCO (V-COCO) is a dataset that builds off COCO for human-object interaction detection. V-COCO provides 10,346 images (2,533 for training, 2,867 for validating and 4,946 for testing) and 16,199 person instances. Each person has annotations for 29 action categories and there are no interaction labels including objects." }, { "dkey": "Kinetics", "dval": "The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube." }, { "dkey": "PA-HMDB51", "dval": "The Privacy Annotated HMDB51 (PA-HMDB51) dataset is a video-based dataset for evaluating pirvacy protection in visual action recognition algorithms. The dataset contains both target task labels (action) and selected privacy attributes (skin color, face, gender, nudity, and relationship) annotated on a per-frame basis." } ]

[ "PHM2017", "Interspeech 2021 Deep Noise Suppression Challenge", "COVERAGE", "GQA", "COCO-Tasks", "ImageNet", "AV Digits Database" ]

[ "COCO", "KITTI" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "KITTI", "dval": "KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist." }, { "dkey": "PHM2017", "dval": "PHM2017 is a new dataset consisting of 7,192 English tweets across six diseases and conditions: Alzheimer’s Disease, heart attack (any severity), Parkinson’s disease, cancer (any type), Depression (any severity), and Stroke. The Twitter search API was used to retrieve the data using the colloquial disease names as search keywords, with the expectation of retrieving a high-recall, low precision dataset. After removing the re-tweets and replies, the tweets were manually annotated. The labels are:\n\n\nself-mention. The tweet contains a health mention with a health self-report of the Twitter account owner, e.g., \"However, I worked hard and ran for Tokyo Mayer Election Campaign in January through February, 2014, without publicizing the cancer.\"\nother-mention. The tweet contains a health mention of a health report about someone other than the account owner, e.g., \"Designer with Parkinson’s couldn’t work then engineer invents bracelet + changes her world\"\nawareness. The tweet contains the disease name, but does not mention a specific person, e.g., \"A Month Before a Heart Attack, Your Body Will Warn You With These 8 Signals\"\nnon-health. The tweet contains the disease name, but the tweet topic is not about health. \"Now I can have cancer on my wall for all to see <3\"" }, { "dkey": "Interspeech 2021 Deep Noise Suppression Challenge", "dval": "The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality.\n\nThis challenge has two two tracks:\n\nTrack 1: Real-Time Denoising track for wide band scenario\n\nThe noise suppressor must take less than the stride time Ts (in ms) to process a frame of size T (in ms) on an Intel Core i5 quad-core machine clocked at 2.4 GHz or equivalent processor. For example, Ts = T/2 for 50% overlap between frames. The total algorithmic latency allowed including the frame size T, stride time Ts, and any look ahead must be less than or equal to 40ms. For example, for a real-time system that receives 20ms audio chunks, if you use a frame length of 20ms with a stride of 10ms resulting in an algorithmic latency of 30ms, then you satisfy the latency requirements. If you use a frame of size 32ms with a stride of 16ms resulting in an algorithmic latency of 48ms, then your method does not satisfy the latency requirements as the total algorithmic latency exceeds 40ms. If your frame size plus stride T1=T+Ts is less than 40ms, then you can use up to (40-T1) ms future information.\n\nTrack 2: Real-Time Denoising track for full band scenario\n\nSatisfy Track 1 requirements but at 48 kHz." }, { "dkey": "COVERAGE", "dval": "COVERAGE contains copymove forged (CMFD) images and their originals with similar but genuine objects (SGOs). COVERAGE is designed to highlight and address tamper detection ambiguity of popular methods, caused by self-similarity within natural images. In COVERAGE, forged–original pairs are annotated with (i) the duplicated and forged region masks, and (ii) the tampering factor/similarity metric. For benchmarking, forgery quality is evaluated using (i) computer vision-based methods, and (ii) human detection performance." }, { "dkey": "GQA", "dval": "The GQA dataset is a large-scale visual question answering dataset with real images from the Visual Genome dataset and balanced question-answer pairs. Each training and validation image is also associated with scene graph annotations describing the classes and attributes of those objects in the scene, and their pairwise relations. Along with the images and question-answer pairs, the GQA dataset provides two types of pre-extracted visual features for each image – convolutional grid features of size 7×7×2048 extracted from a ResNet-101 network trained on ImageNet, and object detection features of size Ndet×2048 (where Ndet is the number of detected objects in each image with a maximum of 100 per image) from a Faster R-CNN detector." }, { "dkey": "COCO-Tasks", "dval": "Comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated." }, { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "AV Digits Database", "dval": "AV Digits Database is an audiovisual database which contains normal, whispered and silent speech. 53 participants were recorded from 3 different views (frontal, 45 and profile) pronouncing digits and phrases in three speech modes.\n\nThe database consists of two parts: digits and short phrases. In the first part, participants were asked to read 10 digits, from 0 to 9, in English in random order five times. In case of non-native English speakers this part was also repeated in the participant’s native language. In total, 53 participants (41 males and 12 females) from 16 nationalities, were recorded with a mean age and standard deviation of 26.7 and 4.3 years, respectively.\n\nIn the second part, participants were asked to read 10 short phrases. The phrases are the same as the ones used in the OuluVS2 database: “Excuse me”, “Goodbye”, “Hello”, “How are you”, “Nice to meet you”, “See you”, “I am sorry”, “Thank you”, “Have a good time”, “You are welcome”. Again, each phrase was repeated five times in 3 different modes, neutral, whisper and silent speech. Thirty nine participants (32 males and 7 females) were recorded for this part with a mean age and standard deviation of 26.3 and 3.8 years, respectively." } ]

[ "iSUN", "E2E", "WN18RR", "DIPS", "DDD20", "ROCStories" ]

[ "ImageNet", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "iSUN", "dval": "iSUN is a ground truth of gaze traces on images from the SUN dataset. The collection is partitioned into 6,000 images for training, 926 for validation and 2,000 for test." }, { "dkey": "E2E", "dval": "End-to-End NLG Challenge (E2E) aims to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena." }, { "dkey": "WN18RR", "dval": "WN18RR is a link prediction dataset created from WN18, which is a subset of WordNet. WN18 consists of 18 relations and 40,943 entities. However, many text triples are obtained by inverting triples from the training set. Thus the WN18RR dataset is created to ensure that the evaluation dataset does not have inverse relation test leakage. In summary, WN18RR dataset contains 93,003 triples with 40,943 entities and 11 relation types." }, { "dkey": "DIPS", "dval": "Contains biases but is two orders of magnitude larger than those used previously." }, { "dkey": "DDD20", "dval": "The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames. DDD20 is the longest event camera end-to-end driving dataset to date with 51h of DAVIS event+frame camera and vehicle human control data collected from 4000km of highway and urban driving under a variety of lighting conditions." }, { "dkey": "ROCStories", "dval": "ROCStories is a collection of commonsense short stories. The corpus consists of 100,000 five-sentence stories. Each story logically follows everyday topics created by Amazon Mechanical Turk workers. These stories contain a variety of commonsense causal and temporal relations between everyday events. Writers also develop an additional 3,742 Story Cloze Test stories which contain a four-sentence-long body and two candidate endings. The endings were collected by asking Mechanical Turk workers to write both a right ending and a wrong ending after eliminating original endings of given short stories. Both endings were required to make logical sense and include at least one character from the main story line. The published ROCStories dataset is constructed with ROCStories as a training set that includes 98,162 stories that exclude candidate wrong endings, an evaluation set, and a test set, which have the same structure (1 body + 2 candidate endings) and a size of 1,871." } ]

[ "FaceForensics", "UMDFaces", "TLP", "SNIPS", "ConvAI2" ]

[ "UCF101", "KITTI" ]

[ { "dkey": "UCF101", "dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240." }, { "dkey": "KITTI", "dval": "KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist." }, { "dkey": "FaceForensics", "dval": "FaceForensics is a video dataset consisting of more than 500,000 frames containing faces from 1004 videos that can be used to study image or video forgeries. All videos are downloaded from Youtube and are cut down to short continuous clips that contain mostly frontal faces. This dataset has two versions:\n\n\n\nSource-to-Target: where the authors reenact over 1000 videos with new facial expressions extracted from other videos, which e.g. can be used to train a classifier to detect fake images or videos.\n\n\n\nSelfreenactment: where the authors use Face2Face to reenact the facial expressions of videos with their own facial expressions as input to get pairs of videos, which e.g. can be used to train supervised generative refinement models." }, { "dkey": "UMDFaces", "dval": "UMDFaces is a face dataset divided into two parts:\n\n\nStill Images - 367,888 face annotations for 8,277 subjects.\nVideo Frames - Over 3.7 million annotated video frames from over 22,000 videos of 3100 subjects.\n\nPart 1 - Still Images\n\nThe dataset contains 367,888 face annotations for 8,277 subjects divided into 3 batches. The annotations contain human curated bounding boxes for faces and estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network.\n\nPart 2 - Video Frames\n\nThe second part contains 3,735,476 annotated video frames extracted from a total of 22,075 for 3,107 subjects. The annotations contain the estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network." }, { "dkey": "TLP", "dval": "A new long video dataset and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." } ]

[ "DukeMTMC-reID", "Airport", "SYSU-MM01", "CUHK-PEDES", "PRID2011", "VeRi-776" ]

[ "Market-1501", "CUHK03" ]

[ { "dkey": "Market-1501", "dval": "Market-1501 is a large-scale public benchmark dataset for person re-identification. It contains 1501 identities which are captured by six different cameras, and 32,668 pedestrian image bounding-boxes obtained using the Deformable Part Models pedestrian detector. Each person has 3.6 images on average at each viewpoint. The dataset is split into two parts: 750 identities are utilized for training and the remaining 751 identities are used for testing. In the official testing protocol 3,368 query images are selected as probe set to find the correct match across 19,732 reference gallery images." }, { "dkey": "CUHK03", "dval": "The CUHK03 consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector. The dataset also provides 20 random train/test splits in which 100 identities are selected for testing and the rest for training" }, { "dkey": "DukeMTMC-reID", "dval": "The DukeMTMC-reID (Duke Multi-Tracking Multi-Camera ReIDentification) dataset is a subset of the DukeMTMC for image-based person re-ID. The dataset is created from high-resolution videos from 8 different cameras. It is one of the largest pedestrian image datasets wherein images are cropped by hand-drawn bounding boxes. The dataset consists 16,522 training images of 702 identities, 2,228 query images of the other 702 identities and 17,661 gallery images.\n\nNOTE: This dataset has been retracted." }, { "dkey": "Airport", "dval": "The Airport dataset is a dataset for person re-identification which consists of 39,902 images and 9,651 identities across six cameras." }, { "dkey": "SYSU-MM01", "dval": "The SYSU-MM01 is a dataset collected for the Visible-Infrared Re-identification problem. The images in the dataset were obtained from 491 different persons by recording them using 4 RGB and 2 infrared cameras. Within the dataset, the persons are divided into 3 fixed splits to create training, validation and test sets. In the training set, there are 20284 RGB and 9929 infrared images of 296 persons. The validation set contains 1974 RGB and 1980 infrared images of 99 persons. The testing set consists of the images of 96 persons where 3803 infrared images are used as query and 301 randomly selected RGB images are used as gallery." }, { "dkey": "CUHK-PEDES", "dval": "The CUHK-PEDES dataset is a caption-annotated pedestrian dataset. It contains 40,206 images over 13,003 persons. Images are collected from five existing person re-identification datasets, CUHK03, Market-1501, SSM, VIPER, and CUHK01 while each image is annotated with 2 text descriptions by crowd-sourcing workers. Sentences incorporate rich details about person appearances, actions, poses." }, { "dkey": "PRID2011", "dval": "PRID 2011 is a person reidentification dataset that provides multiple person trajectories recorded from two different static surveillance cameras, monitoring crosswalks and sidewalks. The dataset shows a clean background, and the people in the dataset are rarely occluded. In the dataset, 200 people appear in both views. Among the 200 people, 178 people have more than 20 appearances" }, { "dkey": "VeRi-776", "dval": "VeRi-776 is a vehicle re-identification dataset which contains 49,357 images of 776 vehicles from 20 cameras. The dataset is collected in the real traffic scenario, which is close to the setting of CityFlow. The dataset contains bounding boxes, types, colors and brands." } ]

[ "TrackingNet", "VOT2017", "MOT17", "VOT2016", "Syn2Real" ]

[ "VOT2018", "LaSOT" ]

[ { "dkey": "VOT2018", "dval": "VOT2018 is a dataset for visual object tracking. It consists of 60 challenging videos collected from real-life datasets." }, { "dkey": "LaSOT", "dval": "LaSOT is a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box, making LaSOT one of the largest densely annotated\ntracking benchmark. The average video length of LaSOT\nis more than 2,500 frames, and each sequence comprises\nvarious challenges deriving from the wild where target objects may disappear and re-appear again in the view." }, { "dkey": "TrackingNet", "dval": "TrackingNet is a large-scale tracking dataset consisting of videos in the wild. It has a total of 30,643 videos split into 30,132 training videos and 511 testing videos, with an average of 470,9 frames." }, { "dkey": "VOT2017", "dval": "VOT2017 is a Visual Object Tracking dataset for different tasks that contains 60 short sequences annotated with 6 different attributes." }, { "dkey": "MOT17", "dval": "The Multiple Object Tracking 17 (MOT17) dataset is a dataset for multiple object tracking. Similar to its previous version MOT16, this challenge contains seven different indoor and outdoor scenes of public places with pedestrians as the objects of interest. A video for each scene is divided into two clips, one for training and the other for testing. The dataset provides detections of objects in the video frames with three detectors, namely SDP, Faster-RCNN and DPM. The challenge accepts both on-line and off-line tracking approaches, where the latter are allowed to use the future video frames to predict tracks." }, { "dkey": "VOT2016", "dval": "VOT2016 is a video dataset for visual object tracking. It contains 60 video clips and 21,646 corresponding ground truth maps with pixel-wise annotation of salient objects." }, { "dkey": "Syn2Real", "dval": "Syn2Real, a synthetic-to-real visual domain adaptation benchmark meant to encourage further development of robust domain transfer methods. The goal is to train a model on a synthetic \"source\" domain and then update it so that its performance improves on a real \"target\" domain, without using any target annotations. It includes three tasks, illustrated in figures above: the more traditional closed-set classification task with a known set of categories; the less studied open-set classification task with unknown object categories in the target domain; and the object detection task, which involves localizing instances of objects by predicting their bounding boxes and corresponding class labels." } ]

[ "MovieFIB", "CLOTH", "CNN/Daily Mail", "SNIPS", "ChID" ]

[ "COCO", "HICO" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "HICO", "dval": "HICO is a benchmark for recognizing human-object interactions (HOI). \n\nKey features:\n\n\nA diverse set of interactions with common object categories\nA list of well-defined, sense-based HOI categories\nAn exhaustive labeling of co-occurring interactions with an object category in each image\nThe annotation of each HOI instance (i.e. a human and an object bounding box with an interaction class label) in all images" }, { "dkey": "MovieFIB", "dval": "A quantitative benchmark for developing and understanding video of fill-in-the-blank question-answering dataset with over 300,000 examples, based on descriptive video annotations for the visually impaired." }, { "dkey": "CLOTH", "dval": "The Cloze Test by Teachers (CLOTH) benchmark is a collection of nearly 100,000 4-way multiple-choice cloze-style questions from middle- and high school-level English language exams, where the answer fills a blank in a given text. Each question is labeled with a type of deep reasoning it involves, where the four possible types are grammar, short-term reasoning, matching/paraphrasing, and long-term reasoning, i.e., reasoning over multiple sentences" }, { "dkey": "CNN/Daily Mail", "dval": "CNN/Daily Mail is a dataset for text summarization. Human generated abstractive summary bullets were generated from news stories in CNN and Daily Mail websites as questions (with one of the entities hidden), and stories as the corresponding passages from which the system is expected to answer the fill-in the-blank question. The authors released the scripts that crawl, extract and generate pairs of passages and questions from these websites.\n\nIn all, the corpus has 286,817 training pairs, 13,368 validation pairs and 11,487 test pairs, as defined by their scripts. The source documents in the training set have 766 words spanning 29.74 sentences on an average while the summaries consist of 53 words and 3.72 sentences." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "ChID", "dval": "ChID is a large-scale Chinese IDiom dataset for cloze test. ChID contains 581K passages and 729K blanks, and covers multiple domains. In ChID, the idioms in a passage were replaced with blank symbols. For each blank, a list of candidate idioms including the golden idiom are provided as choice." } ]

[ "PadChest", "Places", "STARE", "HIGGS Data Set", "ImageNet-P" ]

[ "COCO", "CIFAR-10" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "PadChest", "dval": "PadChest is a labeled large-scale, high resolution chest x-ray dataset for the automated exploration\nof medical images along with their associated reports. This dataset includes more than 160,000\nimages obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital\nSan Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional\ninformation on image acquisition and patient demography. The reports were labeled with 174 different\nradiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical\ntaxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of\nthese reports, 27% were manually annotated by trained physicians and the remaining set was labeled\nusing a supervised method based on a recurrent neural network with attention mechanisms. The labels\ngenerated were then validated in an independent test set achieving a 0.93 Micro-F1 score." }, { "dkey": "Places", "dval": "The Places dataset is proposed for scene recognition and contains more than 2.5 million images covering more than 205 scene categories with more than 5,000 images per category." }, { "dkey": "STARE", "dval": "The STARE (Structured Analysis of the Retina) dataset is a dataset for retinal vessel segmentation. It contains 20 equal-sized (700×605) color fundus images. For each image, two groups of annotations are provided.." }, { "dkey": "HIGGS Data Set", "dval": "The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate the need for physicists to manually develop such features. Benchmark results using Bayesian Decision Trees from a standard physics package and 5-layer neural networks are presented in the original paper. The last 500,000 examples are used as a test set." }, { "dkey": "ImageNet-P", "dval": "ImageNet-P consists of noise, blur, weather, and digital distortions. The dataset has validation perturbations; has difficulty levels; has CIFAR-10, Tiny ImageNet, ImageNet 64 × 64, standard, and Inception-sized editions; and has been designed for benchmarking not training networks. ImageNet-P departs from ImageNet-C by having perturbation sequences generated from each ImageNet validation image. Each sequence contains more than 30 frames, so to counteract an increase in dataset size and evaluation time only 10 common perturbations are used." } ]

[ "SNIPS", "ConvAI2", "Fashion-MNIST", "Wireframe", "I-HAZE", "COVERAGE", "Violin" ]

[ "CIFAR-10", "CelebA" ]

[ { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "CelebA", "dval": "CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "Fashion-MNIST", "dval": "Fashion-MNIST is a dataset comprising of 28×28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST shares the same image size, data format and the structure of training and testing splits with the original MNIST." }, { "dkey": "Wireframe", "dval": "The Wireframe dataset consists of 5,462 images (5,000 for training, 462 for test) of indoor and outdoor man-made scenes." }, { "dkey": "I-HAZE", "dval": "The I-Haze dataset contains 25 indoor hazy images (size 2833×4657 pixels) training. It has 5 hazy images for validation along with their corresponding ground truth images." }, { "dkey": "COVERAGE", "dval": "COVERAGE contains copymove forged (CMFD) images and their originals with similar but genuine objects (SGOs). COVERAGE is designed to highlight and address tamper detection ambiguity of popular methods, caused by self-similarity within natural images. In COVERAGE, forged–original pairs are annotated with (i) the duplicated and forged region masks, and (ii) the tampering factor/similarity metric. For benchmarking, forgery quality is evaluated using (i) computer vision-based methods, and (ii) human detection performance." }, { "dkey": "Violin", "dval": "Video-and-Language Inference is the task of joint multimodal understanding of video and text. Given a video clip with aligned subtitles as premise, paired with a natural language hypothesis based on the video content, a model needs to infer whether the hypothesis is entailed or contradicted by the given video clip. The Violin dataset is a dataset for this task which consists of 95,322 video-hypothesis pairs from 15,887 video clips, spanning over 582 hours of video. These video clips contain rich content with diverse temporal dynamics, event shifts, and people interactions, collected from two sources: (i) popular TV shows, and (ii) movie clips from YouTube channels." } ]

[ "HUMBI", "irc-disentanglement", "SentEval", "PASCAL Face", "Color FERET", "MAFL" ]

[ "CASIA-WebFace", "CelebA" ]

[ { "dkey": "CASIA-WebFace", "dval": "The CASIA-WebFace dataset is used for face verification and face identification tasks. The dataset contains 494,414 face images of 10,575 real identities collected from the web." }, { "dkey": "CelebA", "dval": "CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age." }, { "dkey": "HUMBI", "dval": "A new large multiview dataset for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearance and geometry of gaze, face, hand, body, and garment from assorted people. 107 synchronized HD cameras are used to capture 772 distinctive subjects across gender, ethnicity, age, and physical condition." }, { "dkey": "irc-disentanglement", "dval": "This is a dataset for disentangling conversations on IRC, which is the task of identifying separate conversations in a single stream of messages. It contains disentanglement information for 77,563 messages or IRC." }, { "dkey": "SentEval", "dval": "SentEval is a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. The set of tasks was selected based on what appears to be the community consensus regarding the appropriate evaluations for universal sentence representations. The toolkit comes with scripts to download and preprocess datasets, and an easy interface to evaluate sentence encoders." }, { "dkey": "PASCAL Face", "dval": "The PASCAL FACE dataset is a dataset for face detection and face recognition. It has a total of 851 images which are a subset of the PASCAL VOC and has a total of 1,341 annotations. These datasets contain only a few hundreds of images and have limited variations in face appearance." }, { "dkey": "Color FERET", "dval": "The color FERET database is a dataset for face recognition. It contains 11,338 color images of size 512×768 pixels captured in a semi-controlled environment with 13 different poses from 994 subjects." }, { "dkey": "MAFL", "dval": "The MAFL dataset contains manually annotated facial landmark locations for 19,000 training and 1,000 test images." } ]

[ "AFLW2000-3D", "300-VW", "WFLW", "COVERAGE", "SoF" ]

[ "COFW", "AFW", "AFLW" ]

[ { "dkey": "COFW", "dval": "The Caltech Occluded Faces in the Wild (COFW) dataset is designed to present faces in real-world conditions. Faces show large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (e.g. food, hands, microphones,
etc.). All images were hand annotated using the same 29 landmarks as in LFPW. Both the landmark positions as well as their occluded/unoccluded state were annotated. The faces are occluded to different degrees, with large variations in the type of occlusions encountered. COFW has an average occlusion of over 23." }, { "dkey": "AFW", "dval": "AFW (Annotated Faces in the Wild) is a face detection dataset that contains 205 images with 468 faces. Each face image is labeled with at most 6 landmarks with visibility labels, as well as a bounding box." }, { "dkey": "AFLW", "dval": "The Annotated Facial Landmarks in the Wild (AFLW) is a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total about 25K faces are annotated with up to 21 landmarks per image." }, { "dkey": "AFLW2000-3D", "dval": "AFLW2000-3D is a dataset of 2000 images that have been annotated with image-level 68-point 3D facial landmarks. This dataset is used for evaluation of 3D facial landmark detection models. The head poses are very diverse and often hard to be detected by a CNN-based face detector." }, { "dkey": "300-VW", "dval": "300 Videos in the Wild (300-VW) is a dataset for evaluating facial landmark tracking algorithms in the wild. The dataset authors collected a large number of long facial videos recorded in the wild. Each video has duration of ~1 minute (at 25-30 fps). All frames have been annotated with regards to the same mark-up (i.e. set of facial landmarks) used in the 300 W competition as well (a total of 68 landmarks). The dataset includes 114 videos (circa 1 min each)." }, { "dkey": "WFLW", "dval": "The Wider Facial Landmarks in the Wild or WFLW database contains 10000 faces (7500 for training and 2500 for testing) with 98 annotated landmarks. This database also features rich attribute annotations in terms of occlusion, head pose, make-up, illumination, blur and expressions." }, { "dkey": "COVERAGE", "dval": "COVERAGE contains copymove forged (CMFD) images and their originals with similar but genuine objects (SGOs). COVERAGE is designed to highlight and address tamper detection ambiguity of popular methods, caused by self-similarity within natural images. In COVERAGE, forged–original pairs are annotated with (i) the duplicated and forged region masks, and (ii) the tampering factor/similarity metric. For benchmarking, forgery quality is evaluated using (i) computer vision-based methods, and (ii) human detection performance." }, { "dkey": "SoF", "dval": "The Specs on Faces (SoF) dataset, a collection of 42,592 (2,662×16) images for 112 persons (66 males and 46 females) who wear glasses under different illumination conditions. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is focused on two challenges: harsh illumination environments and face occlusions, which highly affect face detection, recognition, and classification. The glasses are the common natural occlusion in all images of the dataset. However, there are two more synthetic occlusions (nose and mouth) added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty (easy, medium, and hard). That enlarges the number of images to be 42,592 images (26,112 male images and 16,480 female images). There is metadata for each image that contains many information such as: the subject ID, facial landmarks, face and glasses rectangles, gender and age labels, year that the photo was taken, facial emotion, glasses type, and more." } ]

[ "PAQ", "ClariQ", "AQUA", "KnowIT VQA", "HotpotQA" ]

[ "CoQA", "SQuAD" ]

[ { "dkey": "CoQA", "dval": "CoQA is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation.\n\nCoQA contains 127,000+ questions with answers collected from 8000+ conversations. Each conversation is collected by pairing two crowdworkers to chat about a passage in the form of questions and answers. The unique features of CoQA include 1) the questions are conversational; 2) the answers can be free-form text; 3) each answer also comes with an evidence subsequence highlighted in the passage; and 4) the passages are collected from seven diverse domains. CoQA has a lot of challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning." }, { "dkey": "SQuAD", "dval": "The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text. Because the questions and answers are produced by humans through crowdsourcing, it is more diverse than some other question-answering datasets. SQuAD 1.1 contains 107,785 question-answer pairs on 536 articles. SQuAD2.0 (open-domain SQuAD, SQuAD-Open), the latest version, combines the 100,000 questions in SQuAD1.1 with over 50,000 un-answerable questions written adversarially by crowdworkers in forms that are similar to the answerable ones." }, { "dkey": "PAQ", "dval": "Probably Asked Questions (PAQ) is a very large resource of 65M automatically-generated QA-pairs. PAQ is a semi-structured Knowledge Base (KB) of 65M natural language QA-pairs, which models can memorise and/or learn to retrieve from. PAQ differs from traditional KBs in that questions and answers are stored in natural language, and that questions are generated such that they are likely to appear in ODQA datasets. PAQ is automatically constructed using a question generation model and Wikipedia." }, { "dkey": "ClariQ", "dval": "ClariQ is an extension of the Qulac dataset with additional new topics, questions, and answers in the training set. The test set is completely unseen and newly collected. Like Qulac, ClariQ consists of single-turn conversations (initial_request, followed by clarifying question and answer). In addition, it comes with synthetic multi-turn conversations (up to three turns). ClariQ features approximately 18K single-turn conversations, as well as 1.8 million multi-turn conversations." }, { "dkey": "AQUA", "dval": "The question-answer (QA) pairs are automatically generated using state-of-the-art question generation methods based on paintings and comments provided in an existing art understanding dataset. The QA pairs are cleansed by crowdsourcing workers with respect to their grammatical correctness, answerability, and answers' correctness. The dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions." }, { "dkey": "KnowIT VQA", "dval": "KnowIT VQA is a video dataset with 24,282 human-generated question-answer pairs about The Big Bang Theory. The dataset combines visual, textual and temporal coherence reasoning together with knowledge-based questions, which need of the experience obtained from the viewing of the series to be answered." }, { "dkey": "HotpotQA", "dval": "HotpotQA is a question answering dataset collected on the English Wikipedia, containing about 113K crowd-sourced questions that are constructed to require the introduction paragraphs of two Wikipedia articles to answer. Each question in the dataset comes with the two gold paragraphs, as well as a list of sentences in these paragraphs that crowdworkers identify as supporting facts necessary to answer the question. \n\nA diverse range of reasoning strategies are featured in HotpotQA, including questions involving missing entities in the question, intersection questions (What satisfies property A and property B?), and comparison questions, where two entities are compared by a common attribute, among others. In the few-document distractor setting, the QA models are given ten paragraphs in which the gold paragraphs are guaranteed to be found; in the open-domain fullwiki setting, the models are only given the question and the entire Wikipedia. Models are evaluated on their answer accuracy and explainability, where the former is measured as overlap between the predicted and gold answers with exact match (EM) and unigram F1, and the latter concerns how well the predicted supporting fact sentences match human annotation (Supporting Fact EM/F1). A joint metric is also reported on this dataset, which encourages systems to perform well on both tasks simultaneously." } ]

[ "FSDnoisy18k", "FUNSD", "TableBank", "ToTTo", "PieAPP dataset", "FSDKaggle2019" ]

[ "CIFAR-10", "CelebA" ]

[ { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "CelebA", "dval": "CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age." }, { "dkey": "FSDnoisy18k", "dval": "The FSDnoisy18k dataset is an open dataset containing 42.5 hours of audio across 20 sound event classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data. The audio content is taken from Freesound, and the dataset was curated using the Freesound Annotator. The noisy set of FSDnoisy18k consists of 15,813 audio clips (38.8h), and the test set consists of 947 audio clips (1.4h) with correct labels. The dataset features two main types of label noise: in-vocabulary (IV) and out-of-vocabulary (OOV). IV applies when, given an observed label that is incorrect or incomplete, the true or missing label is part of the target class set. Analogously, OOV means that the true or missing label is not covered by those 20 classes." }, { "dkey": "FUNSD", "dval": "Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking." }, { "dkey": "TableBank", "dval": "To address the need for a standard open domain table benchmark dataset, the author propose a novel weak supervision approach to automatically create the TableBank, which is orders of magnitude larger than existing human labeled datasets for table analysis. Distinct from traditional weakly supervised training set, our approach can obtain not only large scale but also high quality training data.\n\nNowadays, there are a great number of electronic documents on the web such as Microsoft Word (.docx) and Latex (.tex) files. These online documents contain mark-up tags for tables in their source code by nature. Intuitively, one can manipulate these source code by adding bounding box using the mark-up language within each document. For Word documents, the internal Office XML code can be modified where the borderline of each table is identified. For Latex documents, the tex code can be also modified where bounding boxes of tables are recognized. In this way, high-quality labeled data is created for a variety of domains such as business documents, official fillings, research papers etc, which is tremendously beneficial for large-scale table analysis tasks.\n\nThe TableBank dataset totally consists of 417,234 high quality labeled tables as well as their original documents in a variety of domains." }, { "dkey": "ToTTo", "dval": "ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.\n\nDuring the dataset creation process, tables from English Wikipedia are matched with (noisy) descriptions. Each table cell mentioned in the description is highlighted and the descriptions are iteratively cleaned and corrected to faithfully reflect the content of the highlighted cells." }, { "dkey": "PieAPP dataset", "dval": "The PieAPP dataset is a large-scale dataset used for training and testing perceptually-consistent image-error prediction algorithms.\nThe dataset can be downloaded from: server containing a zip file with all data (2.2GB) or Google Drive (ideal for quick browsing). \n\nThe dataset contains undistorted high-quality reference images and several distorted versions of these reference images. Pairs of distorted images corresponding to a reference image are labeled with probability of preference labels.\n These labels indicate the fraction of human population that considers one image to be visually closer to the reference over another in the pair.\nTo ensure reliable pairwise probability of preference labels, we query 40 human subjects via Amazon Mechanical Turk for each image pair.\nWe then obtain the percentage of people who selected image A over B as the ground-truth label for this pair, which is the probability of preference of A over B (the supplementary document explains the choice of using 40 human subjects to capture accurate probabilities).\nThis approach is more robust because it is easier to identify the visually closer image than to assign quality scores, and does not suffer from set-dependency or scalability issues like Swiss tournaments since we never label the images with per-image quality scores (see the associated paper and supplementary document for issues with such existing labeling schemes). \nA pairwise learning framework, discussed in the paper, can be used to train image error predictors on the PieAPP dataset.\n\nDataset statistics\nWe make this dataset available for non-commercial and educational purposes only. \nThe dataset contains a total of 200 undistorted reference images, divided into train / validation / test split.\nThese reference images are derived from the Waterloo Exploration Dataset. We release the subset of 200 reference images used in PieAPP from the Waterloo Exploration Dataset with permissions for non-commercial, educational, use from the authors.\nThe users of the PieAPP dataset are requested to cite the Waterloo Exploration Dataset for the reference images, along with PieAPP dataset, as mentioned here.\n\nThe training + validation set contain a total of 160 reference images and test set contains 40 reference images.\nA total of 19,680 distorted images are generated for the train/val set and pairwise probability of preference labels for 77,280 image pairs are made available (derived from querying 40 human subjects for a pairwise comparison + max-likelihood estimation of some missing pairs).\n\nFor test set, 15 distorted images per reference (total 600 distorted images) are created and all possible pairwise comparisons (total 4200) are performed to label each image pair with a probability of preference derived from 40 human subjects' votes.\n\nOverall, the PieAPP dataset provides a total of 20,280 distorted images derived from 200 reference images, and 81,480 pairwise probability-of-preference labels.\n\nMore details of dataset collection can be found in Sec.4 of the paper and supplementary document." }, { "dkey": "FSDKaggle2019", "dval": "FSDKaggle2019 is an audio dataset containing 29,266 audio files annotated with 80 labels of the AudioSet Ontology. FSDKaggle2019 has been used for the DCASE Challenge 2019 Task 2, which was run as a Kaggle competition titled Freesound Audio Tagging 2019. The dataset allows development and evaluation of machine listening methods in conditions of label noise, minimal supervision, and real-world acoustic mismatch. FSDKaggle2019 consists of two train sets and one test set. One train set and the test set consists of manually-labeled data from Freesound, while the other train set consists of noisily labeled web audio data from Flickr videos taken from the YFCC dataset.\nThe curated train set consists of manually labeled data from FSD: 4970 total clips with a total duration of 10.5 hours. The noisy train set has 19,815 clips with a total duration of 80 hours. The test set has 4481 clips with a total duration of 12.9 hours." } ]

[ "CPLFW", "DIML/CVl RGB-D Dataset", "Lost and Found", "DHF1K", "Syn2Real" ]

[ "VIPeR", "CUHK03" ]

[ { "dkey": "VIPeR", "dval": "The Viewpoint Invariant Pedestrian Recognition (VIPeR) dataset includes 632 people and two outdoor cameras under different viewpoints and light conditions. Each person has one image per camera and each image has been scaled to be 128×48 pixels. It provides the pose angle of each person as 0° (front), 45°, 90° (right), 135°, and 180° (back)." }, { "dkey": "CUHK03", "dval": "The CUHK03 consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector. The dataset also provides 20 random train/test splits in which 100 identities are selected for testing and the rest for training" }, { "dkey": "CPLFW", "dval": "A renovation of Labeled Faces in the Wild (LFW), the de facto standard testbed for unconstraint face verification. \n\nThere are three motivations behind the construction of CPLFW benchmark as follows:\n\n1.Establishing a relatively more difficult database to evaluate the performance of real world face verification so the effectiveness of several face verification methods can be fully justified.\n\n2.Continuing the intensive research on LFW with more realistic consideration on pose intra-class variation and fostering the research on cross-pose face verification in unconstrained situation. The challenge of CPLFW emphasizes pose difference to further enlarge intra-class variance. Also, negative pairs are deliberately selected to avoid different gender or race. CPLFW considers both the large intra-class variance and the tiny inter-class variance simultaneously.\n\n3.Maintaining the data size, the face verification protocol which provides a 'same/different' benchmark and the same identities in LFW, so one can easily apply CPLFW to evaluate the performance of face verification." }, { "dkey": "DIML/CVl RGB-D Dataset", "dval": "This dataset contains synchronized RGB-D frames from both Kinect v2 and Zed stereo camera. For the outdoor scene, the authors first generate disparity maps using an accurate stereo matching method and convert them using calibration parameters. A per-pixel confidence map of disparity is also provided. The scenes are captured at various places, e.g., offices, rooms, dormitory, exhibition center, street, road etc., from Yonsei University and Ewha University." }, { "dkey": "Lost and Found", "dval": "Lost and Found is a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic." }, { "dkey": "DHF1K", "dval": "DHF1K is a video saliency dataset which contains a ground-truth map of binary pixel-wise gaze fixation points and a continuous map of the fixation points after being blurred by a gaussian filter. DHF1K contains 1000 videos in total. 700 of the videos are annotated, 600 of which are used for training and 100 for validation. The remaining 300 are the testing set which are to be evaluated on a public server." }, { "dkey": "Syn2Real", "dval": "Syn2Real, a synthetic-to-real visual domain adaptation benchmark meant to encourage further development of robust domain transfer methods. The goal is to train a model on a synthetic \"source\" domain and then update it so that its performance improves on a real \"target\" domain, without using any target annotations. It includes three tasks, illustrated in figures above: the more traditional closed-set classification task with a known set of categories; the less studied open-set classification task with unknown object categories in the target domain; and the object detection task, which involves localizing instances of objects by predicting their bounding boxes and corresponding class labels." } ]

[ "Semantic3D", "Completion3D", "DublinCity", "SynthCity", "2D-3D-S", "2D-3D Match Dataset", "SemanticKITTI" ]

[ "ShapeNet", "KITTI" ]

[ { "dkey": "ShapeNet", "dval": "ShapeNet is a large scale repository for 3D CAD models developed by researchers from Stanford University, Princeton University and the Toyota Technological Institute at Chicago, USA. The repository contains over 300M models with 220,000 classified into 3,135 classes arranged using WordNet hypernym-hyponym relationships. ShapeNet Parts subset contains 31,693 meshes categorised into 16 common object classes (i.e. table, chair, plane etc.). Each shapes ground truth contains 2-5 parts (with a total of 50 part classes)." }, { "dkey": "KITTI", "dval": "KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist." }, { "dkey": "Semantic3D", "dval": "Semantic3D is a point cloud dataset of scanned outdoor scenes with over 3 billion points. It contains 15 training and 15 test scenes annotated with 8 class labels. This large labelled 3D point cloud data set of natural covers a range of diverse urban scenes: churches, streets, railroad tracks, squares, villages, soccer fields, castles to name just a few. The point clouds provided are scanned statically with state-of-the-art equipment and contain very fine details." }, { "dkey": "Completion3D", "dval": "The Completion3D benchmark is a dataset for evaluating state-of-the-art 3D Object Point Cloud Completion methods. Ggiven a partial 3D object point cloud the goal is to infer a complete 3D point cloud for the object." }, { "dkey": "DublinCity", "dval": "A novel benchmark dataset that includes a manually annotated point cloud for over 260 million laser scanning points into 100'000 (approx.) assets from Dublin LiDAR point cloud [12] in 2015. Objects are labelled into 13 classes using hierarchical levels of detail from large (i.e., building, vegetation and ground) to refined (i.e., window, door and tree) elements." }, { "dkey": "SynthCity", "dval": "SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is assigned a label from one of nine categories." }, { "dkey": "2D-3D-S", "dval": "The 2D-3D-S dataset provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations. It covers over 6,000 m2 collected in 6 large-scale indoor areas that originate from 3 different buildings. It contains over 70,000 RGB images, along with the corresponding depths, surface normals, semantic annotations, global XYZ images (all in forms of both regular and 360° equirectangular images) as well as camera information. It also includes registered raw and semantically annotated 3D meshes and point clouds. The dataset enables development of joint and cross-modal learning models and potentially unsupervised approaches utilizing the regularities present in large-scale indoor spaces." }, { "dkey": "2D-3D Match Dataset", "dval": "2D-3D Match Dataset is a new dataset of 2D-3D correspondences by leveraging the availability of several 3D datasets from RGB-D scans. Specifically, the data from SceneNN and 3DMatch are used. The training dataset consists of 110 RGB-D scans, of which 56 scenes are from SceneNN and 54 scenes are from 3DMatch. The 2D-3D correspondence data is generated as follows. Given a 3D point which is randomly sampled from a 3D point cloud, a set of 3D patches from different scanning views are extracted. To find a 2D-3D correspondence, for each 3D patch, its 3D position is re-projected into all RGB-D frames for which the point lies in the camera frustum, taking occlusion into account. The corresponding local 2D patches around the re-projected point are extracted. In total, around 1.4 millions 2D-3D correspondences are collected." }, { "dkey": "SemanticKITTI", "dval": "SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. The dataset consists of 22 sequences. Overall, the dataset provides 23201 point clouds for training and 20351 for testing." } ]

[ "DCASE 2018 Task 4", "LSHTC", "BigEarthNet", "MIND", "OpenEDS", "fMoW" ]

[ "CIFAR-10", "CUHK03" ]

[ { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "CUHK03", "dval": "The CUHK03 consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector. The dataset also provides 20 random train/test splits in which 100 identities are selected for testing and the rest for training" }, { "dkey": "DCASE 2018 Task 4", "dval": "DCASE2018 Task 4 is a dataset for large-scale weakly labeled semi-supervised sound event detection in domestic environments. The data are YouTube video excerpts focusing on domestic context which could be used for example in ambient assisted living applications. The domain was chosen due to the scientific challenges (wide variety of sounds, time-localized events...) and potential industrial applications.\nSpecifically, the task employs a subset of “Audioset: An Ontology And Human-Labeled Dataset For Audio Events” by Google. Audioset consists of an expanding ontology of 632 sound event classes and a collection of 2 million human-labeled 10-second sound clips (less than 21% are shorter than 10-seconds) drawn from 2 million Youtube videos. The ontology is specified as a hierarchical graph of event categories, covering a wide range of human and animal sounds, musical instruments and genres, and common everyday environmental sounds.\nTask 4 focuses on a subset of Audioset that consists of 10 classes of sound events: speech, dog, cat, alarm bell ringing, dishes, frying, blender, running water, vacuum cleaner, electric shaver toothbrush." }, { "dkey": "LSHTC", "dval": "LSHTC is a dataset for large-scale text classification. The data used in the LSHTC challenges originates from two popular sources: the DBpedia and the ODP (Open Directory Project) directory, also known as DMOZ. DBpedia instances were selected from the english, non-regional Extended Abstracts provided by the DBpedia site. The DMOZ instances consist\nof either Content vectors, Description vectors or both. A Content vectors is obtained by directly indexing the web page using standard indexing chain (preprocessing, stemming/lemmatization, stop-word removal)." }, { "dkey": "BigEarthNet", "dval": "BigEarthNet consists of 590,326 Sentinel-2 image patches, each of which is a section of i) 120x120 pixels for 10m bands; ii) 60x60 pixels for 20m bands; and iii) 20x20 pixels for 60m bands." }, { "dkey": "MIND", "dval": "MIcrosoft News Dataset (MIND) is a large-scale dataset for news recommendation research. It was collected from anonymized behavior logs of Microsoft News website. The mission of MIND is to serve as a benchmark dataset for news recommendation and facilitate the research in news recommendation and recommender systems area.\n\nMIND contains about 160k English news articles and more than 15 million impression logs generated by 1 million users. Every news article contains rich textual content including title, abstract, body, category and entities. Each impression log contains the click events, non-clicked events and historical news click behaviors of this user before this impression. To protect user privacy, each user was de-linked from the production system when securely hashed into an anonymized ID." }, { "dkey": "OpenEDS", "dval": "OpenEDS (Open Eye Dataset) is a large scale data set of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152 individual participants and is divided into four subsets: (i) 12,759 images with pixel-level annotations for key eye-regions: iris, pupil and sclera (ii) 252,690 unlabelled eye-images, (iii) 91,200 frames from randomly selected video sequence of 1.5 seconds in duration and (iv) 143 pairs of left and right point cloud data compiled from corneal topography of eye regions collected from a subset, 143 out of 152, participants in the study." }, { "dkey": "fMoW", "dval": "Functional Map of the World (fMoW) is a dataset that aims to inspire the development of machine learning models capable of predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a rich set of metadata features." } ]

[ "STL-10", "LLAMAS", "CASIA V2", "SNIPS", "CC100", "Icentia11K" ]

[ "ImageNet", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "STL-10", "dval": "The STL-10 is an image dataset derived from ImageNet and popularly used to evaluate algorithms of unsupervised feature learning or self-taught learning. Besides 100,000 unlabeled images, it contains 13,000 labeled images from 10 object classes (such as birds, cats, trucks), among which 5,000 images are partitioned for training while the remaining 8,000 images for testing. All the images are color images with 96×96 pixels in size." }, { "dkey": "LLAMAS", "dval": "The unsupervised Labeled Lane MArkerS dataset (LLAMAS) is a dataset for lane detection and segmentation. It contains over 100,000 annotated images, with annotations of over 100 meters at a resolution of 1276 x 717 pixels. The Unsupervised Llamas dataset was annotated by creating high definition maps for automated driving including lane markers based on Lidar. \n\nPaper: Unsupervised Labeled Lane Markers Using Maps" }, { "dkey": "CASIA V2", "dval": "CASIA V2 is a dataset for forgery classification. It contains 4795 images, 1701 authentic and 3274 forged." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "CC100", "dval": "This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository." }, { "dkey": "Icentia11K", "dval": "Public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats." } ]

[ "MOT17", "TAO", "VisDrone", "RicoSCA", "SUN360" ]

[ "COCO", "KITTI" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "KITTI", "dval": "KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist." }, { "dkey": "MOT17", "dval": "The Multiple Object Tracking 17 (MOT17) dataset is a dataset for multiple object tracking. Similar to its previous version MOT16, this challenge contains seven different indoor and outdoor scenes of public places with pedestrians as the objects of interest. A video for each scene is divided into two clips, one for training and the other for testing. The dataset provides detections of objects in the video frames with three detectors, namely SDP, Faster-RCNN and DPM. The challenge accepts both on-line and off-line tracking approaches, where the latter are allowed to use the future video frames to predict tracks." }, { "dkey": "TAO", "dval": "TAO is a federated dataset for Tracking Any Object, containing 2,907 high resolution videos, captured in diverse environments, which are half a minute long on average. A bottom-up approach was used for discovering a large vocabulary of 833 categories, an order of magnitude more than prior tracking benchmarks. \n\nThe dataset was annotated by labelling tracks for objects that move at any point in the video, and giving names to them post factum." }, { "dkey": "VisDrone", "dval": "VisDrone is a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, to make vision meet drones. The VisDrone2019 dataset is collected by the AISKYEYE team at Lab of Machine Learning and Data Mining, Tianjin University, China. The benchmark dataset consists of 288 video clips formed by 261,908 frames and 10,209 static images, captured by various drone-mounted cameras, covering a wide range of aspects including location (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and country), objects (pedestrian, vehicles, bicycles, etc.), and density (sparse and crowded scenes). Note that, the dataset was collected using various drone platforms (i.e., drones with different models), in different scenarios, and under various weather and lighting conditions. These frames are manually annotated with more than 2.6 million bounding boxes of targets of frequent interests, such as pedestrians, cars, bicycles, and tricycles. Some important attributes including scene visibility, object class and occlusion, are also provided for better data utilization." }, { "dkey": "RicoSCA", "dval": "Rico is a public UI corpus with 72K Android UI screens mined from 9.7K Android apps (Deka et al., 2017). Each screen in Rico comes with a screenshot image and a view hierarchy of a collection of UI objects. Authors manually removed screens whose view hierarchies do not match their screenshots by asking annotators to visually verify whether the bounding boxes of view hierarchy leaves match each UI object on the corresponding screenshot image. This filtering results in 25K unique screens.\n\nIn total, RICOSCA contains 295,476 single-step synthetic commands for operating 177,962 different target objects across 25,677 Android screens." }, { "dkey": "SUN360", "dval": "The goal of the SUN360 panorama database is to provide academic researchers in computer vision, computer graphics and computational photography, cognition and neuroscience, human perception, machine learning and data mining, with a comprehensive collection of annotated panoramas covering 360x180-degree full view for a large variety of environmental scenes, places and the objects within. To build the core of the dataset, the authors download a huge number of high-resolution panorama images from the Internet, and group them into different place categories. Then, they designed a WebGL annotation tool for annotating the polygons and cuboids for objects in the scene." } ]

[ "ArCOV-19", "HIGGS Data Set", "SCDB", "D-HAZY", "KITTI-Depth" ]

[ "ImageNet", "CINIC-10", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CINIC-10", "dval": "CINIC-10 is a dataset for image classification. It has a total of 270,000 images, 4.5 times that of CIFAR-10. It is constructed from two different sources: ImageNet and CIFAR-10. Specifically, it was compiled as a bridge between CIFAR-10 and ImageNet. It is split into three equal subsets - train, validation, and test - each of which contain 90,000 images." }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "ArCOV-19", "dval": "ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pandemic that includes over 1M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and -liked)." }, { "dkey": "HIGGS Data Set", "dval": "The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate the need for physicists to manually develop such features. Benchmark results using Bayesian Decision Trees from a standard physics package and 5-layer neural networks are presented in the original paper. The last 500,000 examples are used as a test set." }, { "dkey": "SCDB", "dval": "Includes annotations for 10 distinguishable concepts." }, { "dkey": "D-HAZY", "dval": "The D-HAZY dataset is generated from NYU depth indoor image collection. D-HAZY contains depth map for each indoor hazy image. It contains 1400+ real images and corresponding depth maps used to synthesize hazy scenes based on Koschmieder’s light propagation mode" }, { "dkey": "KITTI-Depth", "dval": "The KITTI-Depth dataset includes depth maps from projected LiDAR point clouds that were matched against the depth estimation from the stereo cameras. The depth images are highly sparse with only 5% of the pixels available and the rest is missing. The dataset has 86k training images, 7k validation images, and 1k test set images on the benchmark server with no access to the ground truth." } ]

[ "RITE", "VOT2018", "MEDIQA-AnS", "IntrA" ]

[ "STARE", "DRIVE" ]

[ { "dkey": "STARE", "dval": "The STARE (Structured Analysis of the Retina) dataset is a dataset for retinal vessel segmentation. It contains 20 equal-sized (700×605) color fundus images. For each image, two groups of annotations are provided.." }, { "dkey": "DRIVE", "dval": "The Digital Retinal Images for Vessel Extraction (DRIVE) dataset is a dataset for retinal vessel segmentation. It consists of a total of JPEG 40 color fundus images; including 7 abnormal pathology cases. The images were obtained from a diabetic retinopathy screening program in the Netherlands. The images were acquired using Canon CR5 non-mydriatic 3CCD camera with FOV equals to 45 degrees. Each image resolution is 584*565 pixels with eight bits per color channel (3 channels). \n\nThe set of 40 images was equally divided into 20 images for the training set and 20 images for the testing set. Inside both sets, for each image, there is circular field of view (FOV) mask of diameter that is approximately 540 pixels. Inside training set, for each image, one manual segmentation by an ophthalmological expert has been applied. Inside testing set, for each image, two manual segmentations have been applied by two different observers, where the first observer segmentation is accepted as the ground-truth for performance evaluation." }, { "dkey": "RITE", "dval": "The RITE (Retinal Images vessel Tree Extraction) is a database that enables comparative studies on segmentation or classification of arteries and veins on retinal fundus images, which is established based on the public available DRIVE database (Digital Retinal Images for Vessel Extraction).\n\nRITE contains 40 sets of images, equally separated into a training subset and a test subset, the same as DRIVE. The two subsets are built from the corresponding two subsets in DRIVE. For each set, there is a fundus photograph, a vessel reference standard, and a Arteries/Veins (A/V) reference standard. \n\n\nThe fundus photograph is inherited from DRIVE. \nFor the training set, the vessel reference standard is a modified version of 1st_manual from DRIVE. \nFor the test set, the vessel reference standard is 2nd_manual from DRIVE. \nFor the A/V reference standard, four types of vessels are labelled using four colors based on the vessel reference standard. \nArteries are labelled in red; veins are labelled in blue; the overlapping of arteries and veins are labelled in green; the vessels which are uncertain are labelled in white. \nThe fundus photograph is in tif format. And the vessel reference standard and the A/V reference standard are in png format. \n\nThe dataset is described in more detail in our paper, which you will cite if you use the dataset in any way: \n\nHu Q, Abràmoff MD, Garvin MK. Automated separation of binary overlapping trees in low-contrast color retinal images. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):436-43. PubMed PMID: 24579170 https://doi.org/10.1007/978-3-642-40763-5_54" }, { "dkey": "VOT2018", "dval": "VOT2018 is a dataset for visual object tracking. It consists of 60 challenging videos collected from real-life datasets." }, { "dkey": "MEDIQA-AnS", "dval": "The first summarization collection containing question-driven summaries of answers to consumer health questions. This dataset can be used to evaluate single or multi-document summaries generated by algorithms using extractive or abstractive approaches." }, { "dkey": "IntrA", "dval": "IntrA is an open-access 3D intracranial aneurysm dataset that makes the application of points-based and mesh-based classification and segmentation models available. This dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction.\n\n103 3D models of entire brain vessels are collected by reconstructing scanned 2D MRA images of patients (the raw 2D MRA images are not published due to medical ethics).\n1909 blood vessel segments are generated automatically from the complete models, including 1694 healthy vessel segments and 215 aneurysm segments for diagnosis.\n116 aneurysm segments are divided and annotated manually by medical experts; the scale of each aneurysm segment is based on the need for a preoperative examination.\nGeodesic distance matrices are computed and included for each annotated 3D segment, because the expression of the geodesic distance is more accurate than Euclidean distance according to the shape of vessels." } ]

[ "SNIPS", "Million-AID", "I-HAZE", "CODEBRIM", "Multi Task Crowd", "ConvAI2" ]

[ "ImageNet", "COCO" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "Million-AID", "dval": "Million-AID is a large-scale benchmark dataset containing a million instances for RS scene classification. There are 51 semantic scene categories in Million-AID. And the scene categories are customized to match the land-use classification standards, which greatly enhance the practicability of the constructed Million-AID. Different form the existing scene classification datasets of which categories are organized with parallel or uncertain relationships, scene categories in Million-AID are organized with systematic relationship architecture, giving it superiority in management and scalability. Specifically, the scene categories in Million-AID are organized by the hierarchical category network of a three-level tree: 51 leaf nodes fall into 28 parent nodes at the second level which are grouped into 8 nodes at the first level, representing the 8 underlying scene categories of agriculture land, commercial land, industrial land, public service land, residential land, transportation land, unutilized land, and water area. The scene category network provides the dataset with excellent organization of relationship among different scene categories and also the property of scalability. The number of images in each scene category ranges from 2,000 to 45,000, endowing the dataset with the property of long tail distribution. Besides, Million-AID has superiorities over the existing scene classification datasets owing to its high spatial resolution, large scale, and global distribution." }, { "dkey": "I-HAZE", "dval": "The I-Haze dataset contains 25 indoor hazy images (size 2833×4657 pixels) training. It has 5 hazy images for validation along with their corresponding ground truth images." }, { "dkey": "CODEBRIM", "dval": "Dataset for multi-target classification of five commonly appearing concrete defects." }, { "dkey": "Multi Task Crowd", "dval": "Multi Task Crowd is a new 100 image dataset fully annotated for crowd counting, violent behaviour detection and density level classification." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." } ]

[ "BDD100K", "CLEVR-Dialog", "WebVision", "Decagon", "iSUN" ]

[ "Flickr30k", "COCO", "SALICON" ]

[ { "dkey": "Flickr30k", "dval": "The Flickr30k dataset contains 31,000 images collected from Flickr, together with 5 reference sentences provided by human annotators." }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "SALICON", "dval": "The SALIency in CONtext (SALICON) dataset contains 10,000 training images, 5,000 validation images and 5,000 test images for saliency prediction. This dataset has been created by annotating saliency in images from MS COCO.\nThe ground-truth saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded.\nThe training and validation sets, provided with ground truth, contain the following data fields: image, resolution and gaze.\nThe testing data contains only the image and resolution fields." }, { "dkey": "BDD100K", "dval": "Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue. More detail is at the dataset home page." }, { "dkey": "CLEVR-Dialog", "dval": "CLEVR-Dialog is a large diagnostic dataset for studying multi-round reasoning in visual dialog. Specifically, that authors construct a dialog grammar that is grounded in the scene graphs of the images from the CLEVR dataset. This combination results in a dataset where all aspects of the visual dialog are fully annotated. In total, CLEVR-Dialog contains 5 instances of 10-round dialogs for about 85k CLEVR images, totaling to 4.25M question-answer pairs.\n\nThe CLEVR-Dialog is used to benchmark performance of standard visual dialog models; in particular, on visual coreference resolution (as a function of the coreference distance). This is the first analysis of its kind for visual dialog models that was not possible without this dataset. \n\nCLEVR-Dialog is aims to help inform the development of future models for visual dialog." }, { "dkey": "WebVision", "dval": "The WebVision dataset is designed to facilitate the research on learning visual representation from noisy web data. It is a large scale web images dataset that contains more than 2.4 million of images crawled from the Flickr website and Google Images search. \n\nThe same 1,000 concepts as the ILSVRC 2012 dataset are used for querying images, such that a bunch of existing approaches can be directly investigated and compared to the models trained from the ILSVRC 2012 dataset, and also makes it possible to study the dataset bias issue in the large scale scenario. The textual information accompanied with those images (e.g., caption, user tags, or description) are also provided as additional meta information. A validation set contains 50,000 images (50 images per category) is provided to facilitate the algorithmic development." }, { "dkey": "Decagon", "dval": "Bio-decagon is a dataset for polypharmacy side effect identification problem framed as a multirelational link prediction problem in a two-layer multimodal graph/network of two node types: drugs and proteins. Protein-protein interaction\nnetwork describes relationships between proteins. Drug-drug interaction network contains 964 different types of edges (one for each side effect type) and describes which drug pairs lead to which side effects. Lastly,\ndrug-protein links describe the proteins targeted by a given drug.\n\nThe final network after linking entity vocabularies used by different databases has 645 drug and 19,085 protein nodes connected by 715,612 protein-protein, 4,651,131 drug-drug, and 18,596 drug-protein edges." }, { "dkey": "iSUN", "dval": "iSUN is a ground truth of gaze traces on images from the SUN dataset. The collection is partitioned into 6,000 images for training, 926 for validation and 2,000 for test." } ]

[ "nuScenes", "2D-3D Match Dataset", "Hollywood 3D dataset", "Completion3D", "SemanticKITTI" ]

[ "SUNCG", "ScanNet" ]

[ { "dkey": "SUNCG", "dval": "SUNCG is a large-scale dataset of synthetic 3D scenes with dense volumetric annotations.\n\nThe dataset is currently not available." }, { "dkey": "ScanNet", "dval": "ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects. Up to now, ScanNet v2, the newest version of ScanNet, has collected 1513 annotated scans with an approximate 90% surface coverage. In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects." }, { "dkey": "nuScenes", "dval": "The nuScenes dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore. Each scene is 20 seconds long and annotated at 2Hz. This results in a total of 28130 samples for training, 6019 samples for validation and 6008 samples for testing. The dataset has the full autonomous vehicle data suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage. The 3D object detection challenge evaluates the performance on 10 classes: cars, trucks, buses, trailers, construction vehicles, pedestrians, motorcycles, bicycles, traffic cones and barriers." }, { "dkey": "2D-3D Match Dataset", "dval": "2D-3D Match Dataset is a new dataset of 2D-3D correspondences by leveraging the availability of several 3D datasets from RGB-D scans. Specifically, the data from SceneNN and 3DMatch are used. The training dataset consists of 110 RGB-D scans, of which 56 scenes are from SceneNN and 54 scenes are from 3DMatch. The 2D-3D correspondence data is generated as follows. Given a 3D point which is randomly sampled from a 3D point cloud, a set of 3D patches from different scanning views are extracted. To find a 2D-3D correspondence, for each 3D patch, its 3D position is re-projected into all RGB-D frames for which the point lies in the camera frustum, taking occlusion into account. The corresponding local 2D patches around the re-projected point are extracted. In total, around 1.4 millions 2D-3D correspondences are collected." }, { "dkey": "Hollywood 3D dataset", "dval": "A dataset for benchmarking action recognition algorithms in natural environments, while making use of 3D information. The dataset contains around 650 video clips, across 14 classes. In addition, two state of the art action recognition algorithms are extended to make use of the 3D data, and five new interest point detection strategies are also proposed, that extend to the 3D data." }, { "dkey": "Completion3D", "dval": "The Completion3D benchmark is a dataset for evaluating state-of-the-art 3D Object Point Cloud Completion methods. Ggiven a partial 3D object point cloud the goal is to infer a complete 3D point cloud for the object." }, { "dkey": "SemanticKITTI", "dval": "SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. The dataset consists of 22 sequences. Overall, the dataset provides 23201 point clouds for training and 20351 for testing." } ]

[ "EMOTIC", "4DFAB", "MegaFace", "BP4D" ]

[ "ImageNet", "AffectNet", "Sports-1M" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "AffectNet", "dval": "AffectNet is a large facial expression dataset with around 0.4 million images manually labeled for the presence of eight (neutral, happy, angry, sad, fear, surprise, disgust, contempt) facial expressions along with the intensity of valence and arousal." }, { "dkey": "Sports-1M", "dval": "The Sports-1M dataset consists of over a million videos from YouTube. The videos in the dataset can be obtained through the YouTube URL specified by the authors. Approximately 7% (as of 2016) of the videos have been removed by the YouTube uploaders since the dataset was compiled. However, there are still over a million videos in the dataset with 487 sports-related categories with 1,000 to 3,000 videos per category. The videos are automatically labelled with 487 sports classes using the YouTube Topics API by analyzing the text metadata associated with the videos (e.g. tags, descriptions). Approximately 5% of the videos are annotated with more than one class." }, { "dkey": "EMOTIC", "dval": "The EMOTIC dataset, named after EMOTions In Context, is a database of images with people in real environments, annotated with their apparent emotions. The images are annotated with an extended list of 26 emotion categories combined with the three common continuous dimensions Valence, Arousal and Dominance." }, { "dkey": "4DFAB", "dval": "4DFAB is a large scale database of dynamic high-resolution 3D faces which consists of recordings of 180 subjects captured in four different sessions spanning over a five-year period (2012 - 2017), resulting in a total of over 1,800,000 3D meshes. It contains 4D videos of subjects displaying both spontaneous and posed facial behaviours. The database can be used for both face and facial expression recognition, as well as behavioural biometrics. It can also be used to learn very powerful blendshapes for parametrising facial behaviour." }, { "dkey": "MegaFace", "dval": "MegaFace was a publicly available dataset which is used for evaluating the performance of face recognition algorithms with up to a million distractors (i.e., up to a million people who are not in the test set). MegaFace contains 1M images from 690K individuals with unconstrained pose, expression, lighting, and exposure. MegaFace captures many different subjects rather than many images of a small number of subjects. The gallery set of MegaFace is collected from a subset of Flickr. The probe set of MegaFace used in the challenge consists of two databases; Facescrub and FGNet. FGNet contains 975 images of 82 individuals, each with several images spanning ages from 0 to 69. Facescrub dataset contains more than 100K face images of 530 people. The MegaFace challenge evaluates performance of face recognition algorithms by increasing the numbers of “distractors” (going from 10 to 1M) in the gallery set. In order to evaluate the face recognition algorithms fairly, MegaFace challenge has two protocols including large or small training sets. If a training set has more than 0.5M images and 20K subjects, it is considered as large. Otherwise, it is considered as small.\n\nNOTE: This dataset has been retired." }, { "dkey": "BP4D", "dval": "The BP4D-Spontaneous dataset is a 3D video database of spontaneous facial expressions in a diverse group of young adults. Well-validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication. Frame-level ground-truth for facial actions was obtained using the Facial Action Coding System. Facial features were tracked in both 2D and 3D domains using both person-specific and generic approaches.\nThe database includes forty-one participants (23 women, 18 men). They were 18 – 29 years of age; 11 were Asian, 6 were African-American, 4 were Hispanic, and 20 were Euro-American. An emotion elicitation protocol was designed to elicit emotions of participants effectively. Eight tasks were covered with an interview process and a series of activities to elicit eight emotions.\nThe database is structured by participants. Each participant is associated with 8 tasks. For each task, there are both 3D and 2D videos. As well, the Metadata include manually annotated action units (FACS AU), automatically tracked head pose, and 2D/3D facial landmarks. The database is in the size of about 2.6TB (without compression)." } ]

[ "NumerSense", "SNIPS", "THEODORE", "CC-Stories" ]

[ "BookCorpus", "SWAG" ]

[ { "dkey": "BookCorpus", "dval": "BookCorpus is a large collection of free novel books written by unpublished authors, which contains 11,038 books (around 74M sentences and 1G words) of 16 different sub-genres (e.g., Romance, Historical, Adventure, etc.)." }, { "dkey": "SWAG", "dval": "Given a partial description like \"she opened the hood of the car,\" humans can reason about the situation and anticipate what might come next (\"then, she examined the engine\"). SWAG (Situations With Adversarial Generations) is a large-scale dataset for this task of grounded commonsense inference, unifying natural language inference and physically grounded reasoning.\n\nThe dataset consists of 113k multiple choice questions about grounded situations. Each question is a video caption from LSMDC or ActivityNet Captions, with four answer choices about what might happen next in the scene. The correct answer is the (real) video caption for the next event in the video; the three incorrect answers are adversarially generated and human verified, so as to fool machines but not humans. The authors aim for SWAG to be a benchmark for evaluating grounded commonsense NLI and for learning representations." }, { "dkey": "NumerSense", "dval": "Contains 13.6k masked-word-prediction probes, 10.5k for fine-tuning and 3.1k for testing." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "THEODORE", "dval": "Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset." }, { "dkey": "CC-Stories", "dval": "CC-Stories (or STORIES) is a dataset for common sense reasoning and language modeling. It was constructed by aggregating documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions in commonsense reasoning tasks. The top 1.0% of highest ranked documents is chosen as the new training corpus." } ]

[ "TrashCan", "ACDC", "PartNet", "SBU Captions Dataset", "DocBank", "A2D" ]

[ "CIHP", "COCO" ]

[ { "dkey": "CIHP", "dval": "The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images. Each image in CIHP is labeled with pixel-wise annotations on 20 categories and instance-level identification. The dataset can be used for the human part segmentation task." }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "TrashCan", "dval": "The TrashCan dataset is an instance-segmentation dataset of underwater trash. It is comprised of annotated images (7,212 images) which contain observations of trash, ROVs, and a wide variety of undersea flora and fauna. The annotations in this dataset take the format of instance segmentation annotations: bitmaps containing a mask marking which pixels in the image contain each object. The imagery in TrashCan is sourced from the J-EDI (JAMSTEC E-Library of Deep-sea Images) dataset, curated by the Japan Agency of Marine Earth Science and Technology (JAMSTEC)." }, { "dkey": "ACDC", "dval": "The goal of the Automated Cardiac Diagnosis Challenge (ACDC) challenge is to:\n\n\ncompare the performance of automatic methods on the segmentation of the left ventricular endocardium and epicardium as the right ventricular endocardium for both end diastolic and end systolic phase instances;\ncompare the performance of automatic methods for the classification of the examinations in five classes (normal case, heart failure with infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, abnormal right ventricle).\n\nThe overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in particular diastolic volume and ejection fraction). The dataset is composed of 150 exams (all from different patients) divided into 5 evenly distributed subgroups (4 pathological plus 1 healthy subject groups) as described below. Furthermore, each patient comes with the following additional information : weight, height, as well as the diastolic and systolic phase instants.\n\nThe database is made available to participants through two datasets from the dedicated online evaluation website after a personal registration: i) a training dataset of 100 patients along with the corresponding manual references based on the analysis of one clinical expert; ii) a testing dataset composed of 50 new patients, without manual annotations but with the patient information given above. The raw input images are provided through the Nifti format." }, { "dkey": "PartNet", "dval": "PartNet is a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information. The dataset consists of 573,585 part instances over 26,671 3D models covering 24 object categories. This dataset enables and serves as a catalyst for many tasks such as shape analysis, dynamic 3D scene modeling and simulation, affordance analysis, and others." }, { "dkey": "SBU Captions Dataset", "dval": "A collection that allows researchers to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results." }, { "dkey": "DocBank", "dval": "A benchmark dataset that contains 500K document pages with fine-grained token-level annotations for document layout analysis. DocBank is constructed using a simple yet effective way with weak supervision from the \\LaTeX{} documents available on the arXiv.com." }, { "dkey": "A2D", "dval": "A2D (Actor-Action Dataset) is a dataset for simultaneously inferring actors and actions in videos. A2D has seven actor classes (adult, baby, ball, bird, car, cat, and dog) and eight action classes (climb, crawl, eat, fly, jump, roll, run, and walk) not including the no-action class, which we also consider. The A2D has 3,782 videos with at least 99 instances per valid actor-action tuple and videos are labeled with both pixel-level actors and actions for sampled frames. The A2D dataset serves as a large-scale testbed for various vision problems: video-level single- and multiple-label actor-action recognition, instance-level object segmentation/co-segmentation, as well as pixel-level actor-action semantic segmentation to name a few." } ]

[ "UMDFaces", "UNSW-NB15", "ConvAI2", "Image and Video Advertisements" ]

[ "MPII", "LSP" ]

[ { "dkey": "MPII", "dval": "The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 different human activities and the poses are manually annotated with up to 16 body joints." }, { "dkey": "LSP", "dval": "The Leeds Sports Pose (LSP) dataset is widely used as the benchmark for human pose estimation. The original LSP dataset contains 2,000 images of sportspersons gathered from Flickr, 1000 for training and 1000 for testing. Each image is annotated with 14 joint locations, where left and right joints are consistently labelled from a person-centric viewpoint. The extended LSP dataset contains additional 10,000 images labeled for training.\n\nImage: Sumer et al" }, { "dkey": "UMDFaces", "dval": "UMDFaces is a face dataset divided into two parts:\n\n\nStill Images - 367,888 face annotations for 8,277 subjects.\nVideo Frames - Over 3.7 million annotated video frames from over 22,000 videos of 3100 subjects.\n\nPart 1 - Still Images\n\nThe dataset contains 367,888 face annotations for 8,277 subjects divided into 3 batches. The annotations contain human curated bounding boxes for faces and estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network.\n\nPart 2 - Video Frames\n\nThe second part contains 3,735,476 annotated video frames extracted from a total of 22,075 for 3,107 subjects. The annotations contain the estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network." }, { "dkey": "UNSW-NB15", "dval": "UNSW-NB15 is a network intrusion dataset. It contains nine different attacks, includes DoS, worms, Backdoors, and Fuzzers. The dataset contains raw network packets. The number of records in the training set is 175,341 records and the testing set is 82,332 records from the different types, attack and normal.\n\nPaper: UNSW-NB15: a comprehensive data set for network intrusion detection systems" }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "Image and Video Advertisements", "dval": "The Image and Video Advertisements collection consists of an image dataset of 64,832 image ads, and a video dataset of 3,477 ads. The data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer (\"What should I do according to this ad, and why should I do it? \"), and symbolic references ads make (e.g. a dove symbolizes peace)." } ]

[ "LS3D-W", "MLFP", "Florence", "SpeakingFaces" ]

[ "AFW", "BP4D", "AFLW" ]

[ { "dkey": "AFW", "dval": "AFW (Annotated Faces in the Wild) is a face detection dataset that contains 205 images with 468 faces. Each face image is labeled with at most 6 landmarks with visibility labels, as well as a bounding box." }, { "dkey": "BP4D", "dval": "The BP4D-Spontaneous dataset is a 3D video database of spontaneous facial expressions in a diverse group of young adults. Well-validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication. Frame-level ground-truth for facial actions was obtained using the Facial Action Coding System. Facial features were tracked in both 2D and 3D domains using both person-specific and generic approaches.\nThe database includes forty-one participants (23 women, 18 men). They were 18 – 29 years of age; 11 were Asian, 6 were African-American, 4 were Hispanic, and 20 were Euro-American. An emotion elicitation protocol was designed to elicit emotions of participants effectively. Eight tasks were covered with an interview process and a series of activities to elicit eight emotions.\nThe database is structured by participants. Each participant is associated with 8 tasks. For each task, there are both 3D and 2D videos. As well, the Metadata include manually annotated action units (FACS AU), automatically tracked head pose, and 2D/3D facial landmarks. The database is in the size of about 2.6TB (without compression)." }, { "dkey": "AFLW", "dval": "The Annotated Facial Landmarks in the Wild (AFLW) is a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total about 25K faces are annotated with up to 21 landmarks per image." }, { "dkey": "LS3D-W", "dval": "A 3D facial landmark dataset of around 230,000 images." }, { "dkey": "MLFP", "dval": "The MLFP dataset consists of face presentation attacks captured with seven 3D latex masks and three 2D print attacks. The dataset contains videos captured from color, thermal and infrared channels." }, { "dkey": "Florence", "dval": "The Florence 3D faces dataset consists of:\n\n\nHigh-resolution 3D scans of human faces from many subjects.\nSeveral video sequences of varying resolution, conditions and zoom level for each subject.\nEach subject is recorded in the following situations:\nIn a controlled setting in HD video.\nIn a less-constrained (but still indoor) setting using a standard, PTZ surveillance camera.\nIn an unconstrained, outdoor environment under challenging recording conditions." }, { "dkey": "SpeakingFaces", "dval": "SpeakingFaces is a publicly-available large-scale dataset developed to support multimodal machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human-computer interaction (HCI), biometric authentication, recognition systems, domain transfer, and speech recognition. SpeakingFaces is comprised of well-aligned high-resolution thermal and visual spectra image streams of fully-framed faces synchronized with audio recordings of each subject speaking approximately 100 imperative phrases." } ]

[ "Airport", "P-DESTRE", "DukeMTMC-reID", "Partial-iLIDS", "SYSU-MM01", "CUHK-PEDES" ]

[ "Market-1501", "CUHK03" ]

[ { "dkey": "Market-1501", "dval": "Market-1501 is a large-scale public benchmark dataset for person re-identification. It contains 1501 identities which are captured by six different cameras, and 32,668 pedestrian image bounding-boxes obtained using the Deformable Part Models pedestrian detector. Each person has 3.6 images on average at each viewpoint. The dataset is split into two parts: 750 identities are utilized for training and the remaining 751 identities are used for testing. In the official testing protocol 3,368 query images are selected as probe set to find the correct match across 19,732 reference gallery images." }, { "dkey": "CUHK03", "dval": "The CUHK03 consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector. The dataset also provides 20 random train/test splits in which 100 identities are selected for testing and the rest for training" }, { "dkey": "Airport", "dval": "The Airport dataset is a dataset for person re-identification which consists of 39,902 images and 9,651 identities across six cameras." }, { "dkey": "P-DESTRE", "dval": "Provides consistent ID annotations across multiple days, making it suitable for the extremely challenging problem of person search, i.e., where no clothing information can be reliably used. Apart this feature, the P-DESTRE annotations enable the research on UAV-based pedestrian detection, tracking, re-identification and soft biometric solutions." }, { "dkey": "DukeMTMC-reID", "dval": "The DukeMTMC-reID (Duke Multi-Tracking Multi-Camera ReIDentification) dataset is a subset of the DukeMTMC for image-based person re-ID. The dataset is created from high-resolution videos from 8 different cameras. It is one of the largest pedestrian image datasets wherein images are cropped by hand-drawn bounding boxes. The dataset consists 16,522 training images of 702 identities, 2,228 query images of the other 702 identities and 17,661 gallery images.\n\nNOTE: This dataset has been retracted." }, { "dkey": "Partial-iLIDS", "dval": "Partial iLIDS is a dataset for occluded person person re-identification. It contains a total of 476 images of 119 people captured by 4 non-overlapping cameras. Some images contain people occluded by other individuals or luggage." }, { "dkey": "SYSU-MM01", "dval": "The SYSU-MM01 is a dataset collected for the Visible-Infrared Re-identification problem. The images in the dataset were obtained from 491 different persons by recording them using 4 RGB and 2 infrared cameras. Within the dataset, the persons are divided into 3 fixed splits to create training, validation and test sets. In the training set, there are 20284 RGB and 9929 infrared images of 296 persons. The validation set contains 1974 RGB and 1980 infrared images of 99 persons. The testing set consists of the images of 96 persons where 3803 infrared images are used as query and 301 randomly selected RGB images are used as gallery." }, { "dkey": "CUHK-PEDES", "dval": "The CUHK-PEDES dataset is a caption-annotated pedestrian dataset. It contains 40,206 images over 13,003 persons. Images are collected from five existing person re-identification datasets, CUHK03, Market-1501, SSM, VIPER, and CUHK01 while each image is annotated with 2 text descriptions by crowd-sourcing workers. Sentences incorporate rich details about person appearances, actions, poses." } ]

[ "DAVANet", "MMAct", "DrivingStereo", "WHU", "NCLS" ]

[ "Middlebury", "KITTI" ]

[ { "dkey": "Middlebury", "dval": "The Middlebury Stereo dataset consists of high-resolution stereo sequences with complex geometry and pixel-accurate ground-truth disparity data. The ground-truth disparities are acquired using a novel technique that employs structured lighting and does not require the calibration of the light projectors." }, { "dkey": "KITTI", "dval": "KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist." }, { "dkey": "DAVANet", "dval": "A large-scale multi-scene dataset for stereo deblurring, containing 20,637 blurry-sharp stereo image pairs from 135 diverse sequences and their corresponding bidirectional disparities." }, { "dkey": "MMAct", "dval": "MMAct is a large-scale dataset for multi/cross modal action understanding. This dataset has been recorded from 20 distinct subjects with seven different types of modalities: RGB videos, keypoints, acceleration, gyroscope, orientation, Wi-Fi and pressure signal. The dataset consists of more than 36k video clips for 37 action classes covering a wide range of daily life activities such as desktop-related and check-in-based ones in four different distinct scenarios." }, { "dkey": "DrivingStereo", "dval": "DrivingStereo contains over 180k images covering a diverse set of driving scenarios, which is hundreds of times larger than the KITTI Stereo dataset. High-quality labels of disparity are produced by a model-guided filtering strategy from multi-frame LiDAR points." }, { "dkey": "WHU", "dval": "Created for MVS tasks and is a large-scale multi-view aerial dataset generated from a highly accurate 3D digital surface model produced from thousands of real aerial images with precise camera parameters." }, { "dkey": "NCLS", "dval": "Presents two high-quality large-scale CLS datasets based on existing monolingual summarization datasets." } ]

[ "Glint360K", "LogiQA", "HellaSwag", "Talk2Car", "Hollywood 3D dataset", "Drive&Act" ]

[ "COCO", "UCF101" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "UCF101", "dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240." }, { "dkey": "Glint360K", "dval": "The largest and cleanest face recognition dataset Glint360K, \nwhich contains 17,091,657 images of 360,232 individuals, baseline models trained on Glint360K can easily achieve state-of-the-art performance." }, { "dkey": "LogiQA", "dval": "LogiQA consists of 8,678 QA instances, covering multiple types of deductive reasoning. Results show that state-of-the-art neural models perform by far worse than human ceiling. The dataset can also serve as a benchmark for reinvestigating logical AI under the deep learning NLP setting." }, { "dkey": "HellaSwag", "dval": "HellaSwag is a challenge dataset for evaluating commonsense NLI that is specially hard for state-of-the-art models, though its questions are trivial for humans (>95% accuracy)." }, { "dkey": "Talk2Car", "dval": "The Talk2Car dataset finds itself at the intersection of various research domains, promoting the development of cross-disciplinary solutions for improving the state-of-the-art in grounding natural language into visual space. The annotations were gathered with the following aspects in mind:\nFree-form high quality natural language commands, that stimulate the development of solutions that can operate in the wild.\nA realistic task setting. Specifically, the authors consider an autonomous driving setting, where a passenger can control the actions of an Autonomous Vehicle by giving commands in natural language.\nThe Talk2Car dataset was build on top of the nuScenes dataset to include an extensive suite of sensor modalities, i.e. semantic maps, GPS, LIDAR, RADAR and 360-degree RGB images annotated with 3D bounding boxes. Such variety of input modalities sets the object referral task on the Talk2Car dataset apart from related challenges, where additional sensor modalities are generally missing." }, { "dkey": "Hollywood 3D dataset", "dval": "A dataset for benchmarking action recognition algorithms in natural environments, while making use of 3D information. The dataset contains around 650 video clips, across 14 classes. In addition, two state of the art action recognition algorithms are extended to make use of the 3D data, and five new interest point detection strategies are also proposed, that extend to the 3D data." }, { "dkey": "Drive&Act", "dval": "The Drive&Act dataset is a state of the art multi modal benchmark for driver behavior recognition. The dataset includes 3D skeletons in addition to frame-wise hierarchical labels of 9.6 Million frames captured by 6 different views and 3 modalities (RGB, IR and depth).\n\nIt offers following key features:\n\n\n12h of video data in 29 long sequences\nCalibrated multi view camera system with 5 views\nMulti modal videos: NIR, Depth and Color data\nMarkerless motion capture: 3D Body Pose and Head Pose\nModel of the static interior of the car\n83 manually annotated hierarchical activity labels:\nLevel 1: Long running tasks (12)\nLevel 2: Semantic actions (34)\nLevel 3: Object Interaction tripplets [action|object|location] (6|17|14)" } ]

[ "AtariARI", "GYAFC", "CARD-660", "SNIPS", "ConvAI2" ]

[ "QNLI", "GLUE" ]

[ { "dkey": "QNLI", "dval": "The QNLI (Question-answering NLI) dataset is a Natural Language Inference dataset automatically derived from the Stanford Question Answering Dataset v1.1 (SQuAD). SQuAD v1.1 consists of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The dataset was converted into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue. The QNLI dataset is part of GLEU benchmark." }, { "dkey": "GLUE", "dval": "General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding tasks, including single-sentence tasks CoLA and SST-2, similarity and paraphrasing tasks MRPC, STS-B and QQP, and natural language inference tasks MNLI, QNLI, RTE and WNLI." }, { "dkey": "AtariARI", "dval": "The AtariARI (Atari Annotated RAM Interface) is an environment for representation learning. The Atari Arcade Learning Environment (ALE) does not explicitly expose any ground truth state information. However, ALE does expose the RAM state (128 bytes per timestep) which are used by the game programmer to store important state information such as the location of sprites, the state of the clock, or the current room the agent is in. To extract these variables, the dataset creators consulted commented disassemblies (or source code) of Atari 2600 games which were made available by Engelhardt and Jentzsch and CPUWIZ. The dataset creators were able to find and verify important state variables for a total of 22 games. Once this information was acquired, combining it with the ALE interface produced a wrapper that can automatically output a state label for every example frame generated from the game. The dataset creators make this available with an easy-to-use gym wrapper, which returns this information with no change to existing code using gym interfaces." }, { "dkey": "GYAFC", "dval": "Grammarly’s Yahoo Answers Formality Corpus (GYAFC) is the largest dataset for any style containing a total of 110K informal / formal sentence pairs.\n\nYahoo Answers is a question answering forum, contains a large number of informal sentences and allows redistribution of data. The authors used the Yahoo Answers L6 corpus to create the GYAFC dataset of informal and formal sentence pairs. In order to ensure a uniform distribution of data, they removed sentences that are questions, contain URLs, and are shorter than 5 words or longer than 25. After these preprocessing steps, 40 million sentences remain. \n\nThe Yahoo Answers corpus consists of several different domains like Business, Entertainment & Music, Travel, Food, etc. Pavlick and Tetreault formality classifier (PT16) shows that the formality level varies significantly\nacross different genres. In order to control for this variation, the authors work with two specific domains that contain the most informal sentences and show results on training and testing within those categories. The authors use the formality classifier from PT16 to identify informal sentences and train this classifier on the Answers genre of the PT16 corpus\nwhich consists of nearly 5,000 randomly selected sentences from Yahoo Answers manually annotated on a scale of -3 (very informal) to 3 (very formal). They find that the domains of Entertainment & Music and Family & Relationships contain the most informal sentences and create the GYAFC dataset using these domains." }, { "dkey": "CARD-660", "dval": "An expert-annotated word similarity dataset which provides a highly reliable, yet challenging, benchmark for rare word representation techniques." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." } ]

[ "T-LESS", "Headlines dataset", "Interspeech 2021 Deep Noise Suppression Challenge", "Replica", "TableBank", "NetHack Learning Environment" ]

[ "AFLW", "300W" ]

[ { "dkey": "AFLW", "dval": "The Annotated Facial Landmarks in the Wild (AFLW) is a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total about 25K faces are annotated with up to 21 landmarks per image." }, { "dkey": "300W", "dval": "The 300-W is a face dataset that consists of 300 Indoor and 300 Outdoor in-the-wild images. It covers a large variation of identity, expression, illumination conditions, pose, occlusion and face size. The images were downloaded from google.com by making queries such as “party”, “conference”, “protests”, “football” and “celebrities”. Compared to the rest of in-the-wild datasets, the 300-W database contains a larger percentage of partially-occluded images and covers more expressions than the common “neutral” or “smile”, such as “surprise” or “scream”.\nImages were annotated with the 68-point mark-up using a semi-automatic methodology. The images of the database were carefully selected so that they represent a characteristic sample of challenging but natural face instances under totally unconstrained conditions. Thus, methods that achieve accurate performance on the 300-W database can demonstrate the same accuracy in most realistic cases.\nMany images of the database contain more than one annotated faces (293 images with 1 face, 53 images with 2 faces and 53 images with [3, 7] faces). Consequently, the database consists of 600 annotated face instances, but 399 unique images. Finally, there is a large variety of face sizes. Specifically, 49.3% of the faces have size in the range [48.6k, 2.0M] and the overall mean size is 85k (about 292 × 292) pixels." }, { "dkey": "T-LESS", "dval": "T-LESS is a dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from simple scenes with several isolated objects to very challenging ones with multiple instances of several objects and with a high amount of clutter and occlusion. The images were captured from a systematically sampled view sphere around the object/scene, and are annotated with accurate ground truth 6D poses of all modeled objects." }, { "dkey": "Headlines dataset", "dval": "The Headlines dataset for sarcasm detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events. The dataset includes all the headlines from News in Brief and News in Photos categories (which are sarcastic) and real (and non-sarcastic) news headlines from HuffPost.\nThis dataset has following advantages over the existing Twitter datasets:\n\n\nSince news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings.\nFurthermore, since the sole purpose of TheOnion is to publish sarcastic news, the dataset has high-quality labels with much less noise as compared to Twitter datasets.\nUnlike tweets which are replies to other tweets, the obtained news headlines are self-contained." }, { "dkey": "Interspeech 2021 Deep Noise Suppression Challenge", "dval": "The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality.\n\nThis challenge has two two tracks:\n\nTrack 1: Real-Time Denoising track for wide band scenario\n\nThe noise suppressor must take less than the stride time Ts (in ms) to process a frame of size T (in ms) on an Intel Core i5 quad-core machine clocked at 2.4 GHz or equivalent processor. For example, Ts = T/2 for 50% overlap between frames. The total algorithmic latency allowed including the frame size T, stride time Ts, and any look ahead must be less than or equal to 40ms. For example, for a real-time system that receives 20ms audio chunks, if you use a frame length of 20ms with a stride of 10ms resulting in an algorithmic latency of 30ms, then you satisfy the latency requirements. If you use a frame of size 32ms with a stride of 16ms resulting in an algorithmic latency of 48ms, then your method does not satisfy the latency requirements as the total algorithmic latency exceeds 40ms. If your frame size plus stride T1=T+Ts is less than 40ms, then you can use up to (40-T1) ms future information.\n\nTrack 2: Real-Time Denoising track for full band scenario\n\nSatisfy Track 1 requirements but at 48 kHz." }, { "dkey": "Replica", "dval": "The Replica Dataset is a dataset of high quality reconstructions of a variety of indoor spaces. Each reconstruction has clean dense geometry, high resolution and high dynamic range textures, glass and mirror surface information, planar segmentation as well as semantic class and instance segmentation." }, { "dkey": "TableBank", "dval": "To address the need for a standard open domain table benchmark dataset, the author propose a novel weak supervision approach to automatically create the TableBank, which is orders of magnitude larger than existing human labeled datasets for table analysis. Distinct from traditional weakly supervised training set, our approach can obtain not only large scale but also high quality training data.\n\nNowadays, there are a great number of electronic documents on the web such as Microsoft Word (.docx) and Latex (.tex) files. These online documents contain mark-up tags for tables in their source code by nature. Intuitively, one can manipulate these source code by adding bounding box using the mark-up language within each document. For Word documents, the internal Office XML code can be modified where the borderline of each table is identified. For Latex documents, the tex code can be also modified where bounding boxes of tables are recognized. In this way, high-quality labeled data is created for a variety of domains such as business documents, official fillings, research papers etc, which is tremendously beneficial for large-scale table analysis tasks.\n\nThe TableBank dataset totally consists of 417,234 high quality labeled tables as well as their original documents in a variety of domains." }, { "dkey": "NetHack Learning Environment", "dval": "The NetHack Learning Environment (NLE) is a Reinforcement Learning environment based on NetHack 3.6.6. It is designed to provide a standard reinforcement learning interface to the game, and comes with tasks that function as a first step to evaluate agents on this new environment.\nNetHack is one of the oldest and arguably most impactful videogames in history, as well as being one of the hardest roguelikes currently being played by humans. It is procedurally generated, rich in entities and dynamics, and overall an extremely challenging environment for current state-of-the-art RL agents, while being much cheaper to run compared to other challenging testbeds. Through NLE, the authors wish to establish NetHack as one of the next challenges for research in decision making and machine learning." } ]

[ "SNIPS", "ROSTD", "MVTecAD", "StreetHazards", "ConvAI2" ]

[ "ImageNet", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "ROSTD", "dval": "A dataset of 4K out-of-domain (OOD) examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, the examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset." }, { "dkey": "MVTecAD", "dval": "MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.\n\nThere are two common metrics: Detection AUROC and Segmentation (or pixelwise) AUROC\n\nDetection (or, classification) methods output single float (anomaly score) per input test image. \n\nSegmentation methods output anomaly probability for each pixel. \n\"To assess segmentation performance, we evaluate the relative per-region overlap of the segmentation with the ground truth. To get an additional performance measure that is independent of the determined threshold, we compute the area under the receiver operating characteristic curve (ROC AUC). We define the true positive rate as the percentage of pixels that were correctly classified as anomalous\" [1]\nLater segmentation metric was improved to balance regions with small and large area, see PRO-AUC and other in [2]\n\n[1] Paul Bergmann et al, \"MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection\"\n[2] Bergmann, P., Batzner, K., Fauser, M. et al. The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. Int J Comput Vis (2021). https://doi.org/10.1007/s11263-020-01400-4" }, { "dkey": "StreetHazards", "dval": "StreetHazards is a synthetic dataset for anomaly detection, created by inserting a diverse array of foreign objects into driving scenes and re-render the scenes with these novel objects." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." } ]

[ "EPIC-KITCHENS-100", "Drive&Act", "BABEL", "A2D", "DISFA", "NTU RGB+D", "FineGym" ]

[ "ActivityNet", "HMDB51", "Breakfast" ]

[ { "dkey": "ActivityNet", "dval": "The ActivityNet dataset contains 200 different types of activities and a total of 849 hours of videos collected from YouTube. ActivityNet is the largest benchmark for temporal activity detection to date in terms of both the number of activity categories and number of videos, making the task particularly challenging. Version 1.3 of the dataset contains 19994 untrimmed videos in total and is divided into three disjoint subsets, training, validation, and testing by a ratio of 2:1:1. On average, each activity category has 137 untrimmed videos. Each video on average has 1.41 activities which are annotated with temporal boundaries. The ground-truth annotations of test videos are not public." }, { "dkey": "HMDB51", "dval": "The HMDB51 dataset is a large collection of realistic videos from various sources, including movies and web videos. The dataset is composed of 6,766 video clips from 51 action categories (such as “jump”, “kiss” and “laugh”), with each category containing at least 101 clips. The original evaluation scheme uses three different training/testing splits. In each split, each action class has 70 clips for training and 30 clips for testing. The average accuracy over these three splits is used to measure the final performance." }, { "dkey": "Breakfast", "dval": "The Breakfast Actions Dataset comprises of 10 actions related to breakfast preparation, performed by 52 different individuals in 18 different kitchens. The dataset is one of the largest fully annotated datasets available. The actions are recorded “in the wild” as opposed to a single controlled lab environment. It consists of over 77 hours of video recordings." }, { "dkey": "EPIC-KITCHENS-100", "dval": "This paper introduces the pipeline to scale the largest dataset in egocentric vision EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (EPIC-KITCHENS-55), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection also enables evaluating the \"test of time\" - i.e. whether models trained on data collected in 2018 can generalise to new footage collected under the same hypotheses albeit \"two years on\".\nThe dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics." }, { "dkey": "Drive&Act", "dval": "The Drive&Act dataset is a state of the art multi modal benchmark for driver behavior recognition. The dataset includes 3D skeletons in addition to frame-wise hierarchical labels of 9.6 Million frames captured by 6 different views and 3 modalities (RGB, IR and depth).\n\nIt offers following key features:\n\n\n12h of video data in 29 long sequences\nCalibrated multi view camera system with 5 views\nMulti modal videos: NIR, Depth and Color data\nMarkerless motion capture: 3D Body Pose and Head Pose\nModel of the static interior of the car\n83 manually annotated hierarchical activity labels:\nLevel 1: Long running tasks (12)\nLevel 2: Semantic actions (34)\nLevel 3: Object Interaction tripplets [action|object|location] (6|17|14)" }, { "dkey": "BABEL", "dval": "BABEL is a large dataset with language labels describing the actions being performed in mocap sequences. BABEL consists of action labels for about 43 hours of mocap sequences from AMASS. Action labels are at two levels of abstraction -- sequence labels describe the overall action in the sequence, and frame labels describe all actions in every frame of the sequence. Each frame label is precisely aligned with the duration of the corresponding action in the mocap sequence, and multiple actions can overlap. There are over 28k sequence labels, and 63k frame labels in BABEL, which belong to over 250 unique action categories. Labels from BABEL can be leveraged for tasks like action recognition, temporal action localization, motion synthesis, etc." }, { "dkey": "A2D", "dval": "A2D (Actor-Action Dataset) is a dataset for simultaneously inferring actors and actions in videos. A2D has seven actor classes (adult, baby, ball, bird, car, cat, and dog) and eight action classes (climb, crawl, eat, fly, jump, roll, run, and walk) not including the no-action class, which we also consider. The A2D has 3,782 videos with at least 99 instances per valid actor-action tuple and videos are labeled with both pixel-level actors and actions for sampled frames. The A2D dataset serves as a large-scale testbed for various vision problems: video-level single- and multiple-label actor-action recognition, instance-level object segmentation/co-segmentation, as well as pixel-level actor-action semantic segmentation to name a few." }, { "dkey": "DISFA", "dval": "The Denver Intensity of Spontaneous Facial Action (DISFA) dataset consists of 27 videos of 4844 frames each, with 130,788 images in total. Action unit annotations are on different levels of intensity, which are ignored in the following experiments and action units are either set or unset. DISFA was selected from a wider range of databases popular in the field of facial expression recognition because of the high number of smiles, i.e. action unit 12. In detail, 30,792 have this action unit set, 82,176 images have some action unit(s) set and 48,612 images have no action unit(s) set at all." }, { "dkey": "NTU RGB+D", "dval": "NTU RGB+D is a large-scale dataset for RGB-D human action recognition. It involves 56,880 samples of 60 action classes collected from 40 subjects. The actions can be generally divided into three categories: 40 daily actions (e.g., drinking, eating, reading), nine health-related actions (e.g., sneezing, staggering, falling down), and 11 mutual actions (e.g., punching, kicking, hugging). These actions take place under 17 different scene conditions corresponding to 17 video sequences (i.e., S001–S017). The actions were captured using three cameras with different horizontal imaging viewpoints, namely, −45∘,0∘, and +45∘. Multi-modality information is provided for action characterization, including depth maps, 3D skeleton joint position, RGB frames, and infrared sequences. The performance evaluation is performed by a cross-subject test that split the 40 subjects into training and test groups, and by a cross-view test that employed one camera (+45∘) for testing, and the other two cameras for training." }, { "dkey": "FineGym", "dval": "FineGym is an action recognition dataset build on top of gymnasium videos. Compared to existing action recognition datasets, FineGym is distinguished in richness, quality, and diversity. In particular, it provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy. For example, a \"balance beam\" event will be annotated as a sequence of elementary sub-actions derived from five sets: \"leap-jumphop\", \"beam-turns\", \"flight-salto\", \"flight-handspring\", and \"dismount\", where the sub-action in each set will be further annotated with finely defined class labels. This new level of granularity presents significant challenges for action recognition, e.g. how to parse the temporal structures from a coherent action, and how to distinguish between subtly different action classes." } ]

[ "THEODORE", "NAB", "Open Images V4", "OccludedPASCAL3D+" ]

[ "BSDS500", "SBD" ]

[ { "dkey": "BSDS500", "dval": "Berkeley Segmentation Data Set 500 (BSDS500) is a standard benchmark for contour detection. This dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries. It includes 500 natural images with carefully annotated boundaries collected from multiple users. The dataset is divided into three parts: 200 for training, 100 for validation and the rest 200 for test." }, { "dkey": "SBD", "dval": "The Semantic Boundaries Dataset (SBD) is a dataset for predicting pixels on the boundary of the object (as opposed to the inside of the object with semantic segmentation). The dataset consists of 11318 images from the trainval set of the PASCAL VOC2011 challenge, divided into 8498 training and 2820 test images. This dataset has object instance boundaries with accurate figure/ground masks that are also labeled with one of 20 Pascal VOC classes." }, { "dkey": "THEODORE", "dval": "Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset." }, { "dkey": "NAB", "dval": "The First Temporal Benchmark Designed to Evaluate Real-time Anomaly Detectors Benchmark\n\nThe growth of the Internet of Things has created an abundance of streaming data. Finding anomalies in this data can provide valuable insights into opportunities or failures. Yet it’s difficult to achieve, due to the need to process data in real time, continuously learn and make predictions. How do we evaluate and compare various real-time anomaly detection techniques? \n\nThe Numenta Anomaly Benchmark (NAB) provides a standard, open source framework for evaluating real-time anomaly detection algorithms on streaming data. Through a controlled, repeatable environment of open-source tools, NAB rewards detectors that find anomalies as soon as possible, trigger no false alarms, and automatically adapt to any changing statistics. \n\nNAB comprises two main components: a scoring system designed for streaming data and a dataset with labeled, real-world time-series data." }, { "dkey": "Open Images V4", "dval": "Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, 15x more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images) are provided. The images often show complex scenes with several objects (8 annotated objects per image on average). Visual relationships between them are annotated, which support visual relationship detection, an emerging task that requires structured reasoning." }, { "dkey": "OccludedPASCAL3D+", "dval": "The OccludedPASCAL3D+ is a dataset is designed to evaluate the robustness to occlusion for a number of computer vision tasks, such as object detection, keypoint detection and pose estimation. In the OccludedPASCAL3D+ dataset, we simulate partial occlusion by superimposing objects cropped from the MS-COCO dataset on top of objects from the PASCAL3D+ dataset. We only use ImageNet subset in PASCAL3D+, which has 10812 testing images." } ]

[ "RAF-DB", "RobustPointSet", "OLID", "PadChest", "Horne 2017 Fake News Data" ]

[ "VOT2018", "VOT2017", "VOT2016" ]

[ { "dkey": "VOT2018", "dval": "VOT2018 is a dataset for visual object tracking. It consists of 60 challenging videos collected from real-life datasets." }, { "dkey": "VOT2017", "dval": "VOT2017 is a Visual Object Tracking dataset for different tasks that contains 60 short sequences annotated with 6 different attributes." }, { "dkey": "VOT2016", "dval": "VOT2016 is a video dataset for visual object tracking. It contains 60 video clips and 21,646 corresponding ground truth maps with pixel-wise annotation of salient objects." }, { "dkey": "RAF-DB", "dval": "The Real-world Affective Faces Database (RAF-DB) is a dataset for facial expression. It contains 29672 facial images tagged with basic or compound expressions by 40 independent taggers. Images in this database are of great variability in subjects' age, gender and ethnicity, head poses, lighting conditions, occlusions, (e.g. glasses, facial hair or self-occlusion), post-processing operations (e.g. various filters and special effects), etc." }, { "dkey": "RobustPointSet", "dval": "A dataset for robustness analysis of point cloud classification models (independent of data augmentation) to input transformations." }, { "dkey": "OLID", "dval": "The OLID is a hierarchical dataset to identify the type and the target of offensive texts in social media. The dataset is collected on Twitter and publicly available. There are 14,100 tweets in total, in which 13,240 are in the training set, and 860 are in the test set. For each tweet, there are three levels of labels: (A) Offensive/Not-Offensive, (B) Targeted-Insult/Untargeted, (C) Individual/Group/Other. The relationship between them is hierarchical. If a tweet is offensive, it can have a target or no target. If it is offensive to a specific target, the target can be an individual, a group, or some other objects. This dataset is used in the OffensEval-2019 competition in SemEval-2019." }, { "dkey": "PadChest", "dval": "PadChest is a labeled large-scale, high resolution chest x-ray dataset for the automated exploration\nof medical images along with their associated reports. This dataset includes more than 160,000\nimages obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital\nSan Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional\ninformation on image acquisition and patient demography. The reports were labeled with 174 different\nradiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical\ntaxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of\nthese reports, 27% were manually annotated by trained physicians and the remaining set was labeled\nusing a supervised method based on a recurrent neural network with attention mechanisms. The labels\ngenerated were then validated in an independent test set achieving a 0.93 Micro-F1 score." }, { "dkey": "Horne 2017 Fake News Data", "dval": "The Horne 2017 Fake News Data contains two independed news datasets:\n\n\n\nBuzzfeed Political News Data:\n\n\nNews originally analyzed by Craig Silverman of Buzzfeed News in article entitled \" This Analysis Shows How Viral Fake Election News Stories Outperformed Real News On Facebook.\"\nBuzzFeed News used keyword search on the content analysis tool BuzzSumo to find news stories\nPost the analysis of Buzzfeed News, the authors collect the body text and body title of all articles and use the ground truth as set by Buzzfeed as actual ground truth.\nThis data set has fewer clear restrictions on the ground truth, including opinion-based real stories and satire-based fake stories. In our study, the authors manually filter this data set down to contain only \"hard\" news stories and malicious fake news stories. This repository contains the whole dataset with no filtering.\n\n\n\nRandom Political News Data:\n\n\nRandomly collected from three types of sources during 2016.\nSources ground truth determined through: Business Insider’s “Most Trusted” list and Zimdars 2016 Fake news list\nSources:\nReal: Wall Street Journal, The Economist, BBC, NPR, ABC, CBS, USA Today, The Guardian, NBC, The Washington Post\nSatire: The Onion, Huffington Post Satire, Borowitz Report, The Beaverton, Satire Wire, and Faking News\nFake: Ending The Fed, True Pundit, abcnews.com.co, DC Gazette, Liberty Writers News, Before its News, InfoWars, Real News Right Now" } ]

[ "D-HAZY", "Virtual KITTI", "RicoSCA", "Deep Fashion3D", "Shiny dataset" ]

[ "ShapeNet", "KITTI" ]

[ { "dkey": "ShapeNet", "dval": "ShapeNet is a large scale repository for 3D CAD models developed by researchers from Stanford University, Princeton University and the Toyota Technological Institute at Chicago, USA. The repository contains over 300M models with 220,000 classified into 3,135 classes arranged using WordNet hypernym-hyponym relationships. ShapeNet Parts subset contains 31,693 meshes categorised into 16 common object classes (i.e. table, chair, plane etc.). Each shapes ground truth contains 2-5 parts (with a total of 50 part classes)." }, { "dkey": "KITTI", "dval": "KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist." }, { "dkey": "D-HAZY", "dval": "The D-HAZY dataset is generated from NYU depth indoor image collection. D-HAZY contains depth map for each indoor hazy image. It contains 1400+ real images and corresponding depth maps used to synthesize hazy scenes based on Koschmieder’s light propagation mode" }, { "dkey": "Virtual KITTI", "dval": "Virtual KITTI is a photo-realistic synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation, optical flow, and depth estimation.\n\nVirtual KITTI contains 50 high-resolution monocular videos (21,260 frames) generated from five different virtual worlds in urban settings under different imaging and weather conditions. These worlds were created using the Unity game engine and a novel real-to-virtual cloning method. These photo-realistic synthetic videos are automatically, exactly, and fully annotated for 2D and 3D multi-object tracking and at the pixel level with category, instance, flow, and depth labels (cf. below for download links)." }, { "dkey": "RicoSCA", "dval": "Rico is a public UI corpus with 72K Android UI screens mined from 9.7K Android apps (Deka et al., 2017). Each screen in Rico comes with a screenshot image and a view hierarchy of a collection of UI objects. Authors manually removed screens whose view hierarchies do not match their screenshots by asking annotators to visually verify whether the bounding boxes of view hierarchy leaves match each UI object on the corresponding screenshot image. This filtering results in 25K unique screens.\n\nIn total, RICOSCA contains 295,476 single-step synthetic commands for operating 177,962 different target objects across 25,677 Android screens." }, { "dkey": "Deep Fashion3D", "dval": "A novel benchmark and dataset for the evaluation of image-based garment reconstruction systems. Deep Fashion3D contains 2078 models reconstructed from real garments, which covers 10 different categories and 563 garment instances. It provides rich annotations including 3D feature lines, 3D body pose and the corresponded multi-view real images. In addition, each garment is randomly posed to enhance the variety of real clothing deformations." }, { "dkey": "Shiny dataset", "dval": "The shiny folder contains 8 scenes with challenging view-dependent effects used in our paper. We also provide additional scenes in the shiny_extended folder. \nThe test images for each scene used in our paper consist of one of every eight images in alphabetical order.\n\nEach scene contains the following directory structure:\nscene/\n dense/\n cameras.bin\n images.bin\n points3D.bin\n project.ini\n images/\n image_name1.png\n image_name2.png\n ...\n image_nameN.png\n images_distort/\n image_name1.png\n image_name2.png\n ...\n image_nameN.png\n sparse/\n cameras.bin\n images.bin\n points3D.bin\n project.ini\n database.db\n hwf_cxcy.npy\n planes.txt\n poses_bounds.npy\n\n\ndense/ folder contains COLMAP's output [1] after the input images are undistorted.\nimages/ folder contains undistorted images. (We use these images in our experiments.)\nimages_distort/ folder contains raw images taken from a smartphone.\nsparse/ folder contains COLMAP's sparse reconstruction output [1].\n\nOur poses_bounds.npy is similar to the LLFF[2] file format with a slight modification. This file stores a Nx14 numpy array, where N is the number of cameras. Each row in this array is split into two parts of sizes 12 and 2. The first part, when reshaped into 3x4, represents the camera extrinsic (camera-to-world transformation), and the second part with two dimensions stores the distances from that point of view to the first and last planes (near, far). These distances are computed automatically based on the scene’s statistics using LLFF’s code. (For details on how these are computed, see this code) \n\nhwf_cxcy.npy stores the camera intrinsic (height, width, focal length, principal point x, principal point y) in a 1x5 numpy array.\n\nplanes.txt stores information about the MPI planes. The first two numbers are the distances from a reference camera to the first and last planes (near, far). The third number tells whether the planes are placed equidistantly in the depth space (0) or inverse depth space (1). The last number is the padding size in pixels on all four sides of each of the MPI planes. I.e., the total dimension of each plane is (H + 2 * padding, W + 2 * padding).\n\nReferences:\n\n\n[1]: COLMAP structure from motion (Schönberger and Frahm, 2016).\n[2]: Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines (Mildenhall et al., 2019)." } ]

[ "UNITOPATHO", "WGISD", "Places", "WikiReading", "30MQA" ]

[ "ImageNet", "WikiText-2" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "WikiText-2", "dval": "The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.\n\nCompared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 times larger. The WikiText dataset also features a far larger vocabulary and retains the original case, punctuation and numbers - all of which are removed in PTB. As it is composed of full articles, the dataset is well suited for models that can take advantage of long term dependencies." }, { "dkey": "UNITOPATHO", "dval": "Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin stained patches extracted from 292 whole-slide images, meant for training deep neural networks for colorectal polyps classification and adenomas grading. The slides are acquired through a Hamamatsu Nanozoomer S210 scanner at 20× magnification (0.4415 μm/px)" }, { "dkey": "WGISD", "dval": "Embrapa Wine Grape Instance Segmentation Dataset (WGISD) contains grape clusters properly annotated in 300 images and a novel annotation methodology for segmentation of complex objects in natural images." }, { "dkey": "Places", "dval": "The Places dataset is proposed for scene recognition and contains more than 2.5 million images covering more than 205 scene categories with more than 5,000 images per category." }, { "dkey": "WikiReading", "dval": "WikiReading is a large-scale natural language understanding task and publicly-available dataset with 18 million instances. The task is to predict textual values from the structured knowledge base Wikidata by reading the text of the corresponding Wikipedia articles. The task contains a rich variety of challenging classification and extraction sub-tasks, making it well-suited for end-to-end models such as deep neural networks (DNNs)." }, { "dkey": "30MQA", "dval": "An enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions." } ]

[ "SNIPS", "ConvAI2", "COCO Captions", "NeuralNews", "Stanford Cars", "TextCaps", "ActivityNet Entities" ]

[ "Flickr30k", "COCO" ]

[ { "dkey": "Flickr30k", "dval": "The Flickr30k dataset contains 31,000 images collected from Flickr, together with 5 reference sentences provided by human annotators." }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "COCO Captions", "dval": "COCO Captions contains over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions are be provided for each image." }, { "dkey": "NeuralNews", "dval": "NeuralNews is a dataset for machine-generated news detection. It consists of human-generated and machine-generated articles. The human-generated articles are extracted from the GoodNews dataset, which is extracted from the New York Times. It contains 4 types of articles:\n\n\nReal Articles and Real Captions\nReal Articles and Generated Captions\nGenerated Articles and Real Captions\nGenerated Articles and Generated Captions\n\nIn total, it contains about 32K samples of each article type (resulting in about 128K total)." }, { "dkey": "Stanford Cars", "dval": "The Stanford Cars dataset consists of 196 classes of cars with a total of 16,185 images, taken from the rear. The data is divided into almost a 50-50 train/test split with 8,144 training images and 8,041 testing images. Categories are typically at the level of Make, Model, Year. The images are 360×240." }, { "dkey": "TextCaps", "dval": "Contains 145k captions for 28k images. The dataset challenges a model to recognize text, relate it to its visual context, and decide what part of the text to copy or paraphrase, requiring spatial, semantic, and visual reasoning between multiple text tokens and visual entities, such as objects." }, { "dkey": "ActivityNet Entities", "dval": "ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase. This allows training video description models with this data, and importantly, evaluate how grounded or \"true\" such model are to the video they describe." } ]

[ "SNIPS", "ConvAI2", "BigEarthNet", "COCO-Tasks", "CLUECorpus2020" ]

[ "COCO", "Matterport3D" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "Matterport3D", "dval": "The Matterport3D dataset is a large RGB-D dataset for scene understanding in indoor environments. It contains 10,800 panoramic views inside 90 real building-scale scenes, constructed from 194,400 RGB-D images. Each scene is a residential building consisting of multiple rooms and floor levels, and is annotated with surface construction, camera poses, and semantic segmentation." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "BigEarthNet", "dval": "BigEarthNet consists of 590,326 Sentinel-2 image patches, each of which is a section of i) 120x120 pixels for 10m bands; ii) 60x60 pixels for 20m bands; and iii) 20x20 pixels for 60m bands." }, { "dkey": "COCO-Tasks", "dval": "Comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated." }, { "dkey": "CLUECorpus2020", "dval": "CLUECorpus2020 is a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language generation. It has 100G raw corpus with 35 billion Chinese characters, which is retrieved from Common Crawl." } ]

[ "PAQ", "CLUECorpus2020", "Flightmare Simulator", "Scan2CAD", "IMPPRES", "SNIPS" ]

[ "ImageNet", "COCO" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "PAQ", "dval": "Probably Asked Questions (PAQ) is a very large resource of 65M automatically-generated QA-pairs. PAQ is a semi-structured Knowledge Base (KB) of 65M natural language QA-pairs, which models can memorise and/or learn to retrieve from. PAQ differs from traditional KBs in that questions and answers are stored in natural language, and that questions are generated such that they are likely to appear in ODQA datasets. PAQ is automatically constructed using a question generation model and Wikipedia." }, { "dkey": "CLUECorpus2020", "dval": "CLUECorpus2020 is a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language generation. It has 100G raw corpus with 35 billion Chinese characters, which is retrieved from Common Crawl." }, { "dkey": "Flightmare Simulator", "dval": "Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently from each other. Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) an integration with a virtual-reality headset for interaction with the simulated environment. Flightmare can be used for various applications, including path-planning, reinforcement learning, visual-inertial odometry, deep learning, human-robot interaction, etc." }, { "dkey": "Scan2CAD", "dval": "Scan2CAD is an alignment dataset based on 1506 ScanNet scans with 97607 annotated keypoints pairs between 14225 (3049 unique) CAD models from ShapeNet and their counterpart objects in the scans. The top 3 annotated model classes are chairs, tables and cabinets which arises due to the nature of indoor scenes in ScanNet. The number of objects aligned per scene ranges from 1 to 40 with an average of 9.3.\n\nAdditionally, all ShapeNet CAD models used in the Scan2CAD dataset are annotated with their rotational symmetries: either none, 2-fold, 4-fold or infinite rotational symmetries around a canonical axis of the object." }, { "dkey": "IMPPRES", "dval": "An IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." } ]

[ "EMOTIC", "SAVEE", "Microsoft Research Social Media Conversation Corpus", "SEND" ]

[ "SFEW", "ORL" ]

[ { "dkey": "SFEW", "dval": "The Static Facial Expressions in the Wild (SFEW) dataset is a dataset for facial expression recognition. It was created by selecting static frames from the AFEW database by computing key frames based on facial point clustering. The most commonly used version, SFEW 2.0, was the benchmarking data for the SReco sub-challenge in EmotiW 2015. SFEW 2.0 has been divided into three sets: Train (958 samples), Val (436 samples) and Test (372 samples). Each of the images is assigned to one of seven expression categories, i.e., anger, disgust, fear, neutral, happiness, sadness, and surprise. The expression labels of the training and validation sets are publicly available, whereas those of the testing set are held back by the challenge organizer." }, { "dkey": "ORL", "dval": "The ORL Database of Faces contains 400 images from 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). The size of each image is 92x112 pixels, with 256 grey levels per pixel.\n\nDownload dataset from Kaggle: https://www.kaggle.com/datasets/kasikrit/att-database-of-faces" }, { "dkey": "EMOTIC", "dval": "The EMOTIC dataset, named after EMOTions In Context, is a database of images with people in real environments, annotated with their apparent emotions. The images are annotated with an extended list of 26 emotion categories combined with the three common continuous dimensions Valence, Arousal and Dominance." }, { "dkey": "SAVEE", "dval": "The Surrey Audio-Visual Expressed Emotion (SAVEE) dataset was recorded as a pre-requisite for the development of an automatic emotion recognition system. The database consists of recordings from 4 male actors in 7 different emotions, 480 British English utterances in total. The sentences were chosen from the standard TIMIT corpus and phonetically-balanced for each emotion. The data were recorded in a visual media lab with high quality audio-visual equipment, processed and labeled. To check the quality of performance, the recordings were evaluated by 10 subjects under audio, visual and audio-visual conditions. Classification systems were built using standard features and classifiers for each of the audio, visual and audio-visual modalities, and speaker-independent recognition rates of 61%, 65% and 84% achieved respectively." }, { "dkey": "Microsoft Research Social Media Conversation Corpus", "dval": "Microsoft Research Social Media Conversation Corpus consists of 127M context-message-response triples from the Twitter FireHose, covering the 3-month period June 2012 through August 2012. Only those triples where context and response were generated by the same user were extracted. To minimize noise, only triples that contained at least one frequent bigram that appeared more than 3 times in the corpus was selected. This produced a corpus of 29M Twitter triples." }, { "dkey": "SEND", "dval": "SEND (Stanford Emotional Narratives Dataset) is a set of rich, multimodal videos of self-paced, unscripted emotional narratives, annotated for emotional valence over time. The complex narratives and naturalistic expressions in this dataset provide a challenging test for contemporary time-series emotion recognition models." } ]

[ "ASSET", "Penn Treebank", "ConvAI2", "GSL", "ASSET Corpus", "TextComplexityDE", "VIST" ]

[ "SNLI", "MultiNLI", "WikiLarge" ]

[ { "dkey": "SNLI", "dval": "The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and asked to generate entailing, contradicting, and neutral sentences. Annotators were instructed to judge the relation between sentences given that they describe the same event. Each pair is labeled as “entailment”, “neutral”, “contradiction” or “-”, where “-” indicates that an agreement could not be reached." }, { "dkey": "MultiNLI", "dval": "The Multi-Genre Natural Language Inference (MultiNLI) dataset has 433K sentence pairs. Its size and mode of collection are modeled closely like SNLI. MultiNLI offers ten distinct genres (Face-to-face, Telephone, 9/11, Travel, Letters, Oxford University Press, Slate, Verbatim, Goverment and Fiction) of written and spoken English data. There are matched dev/test sets which are derived from the same sources as those in the training set, and mismatched sets which do not closely resemble any seen at training time." }, { "dkey": "WikiLarge", "dval": "WikiLarge comprise 359 test sentences, 2000 development sentences and 300k training sentences. Each source sentences in test set has 8 simplified references" }, { "dkey": "ASSET", "dval": "ASSET is a new dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations." }, { "dkey": "Penn Treebank", "dval": "The English Penn Treebank (PTB) corpus, and in particular the section of the corpus corresponding to the articles of Wall Street Journal (WSJ), is one of the most known and used corpus for the evaluation of models for sequence labelling. The task consists of annotating each word with its Part-of-Speech tag. In the most common split of this corpus, sections from 0 to 18 are used for training (38 219 sentences, 912 344 tokens), sections from 19 to 21 are used for validation (5 527 sentences, 131 768 tokens), and sections from 22 to 24 are used for testing (5 462 sentences, 129 654 tokens).\nThe corpus is also commonly used for character-level and word-level Language Modelling." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "GSL", "dval": "Dataset Description\nThe Greek Sign Language (GSL) is a large-scale RGB+D dataset, suitable for Sign Language Recognition (SLR) and Sign Language Translation (SLT). The video captures are conducted using an Intel RealSense D435 RGB+D camera at a rate of 30 fps. Both the RGB and the depth streams are acquired in the same spatial resolution of 848×480 pixels. To increase variability in the videos, the camera position and orientation is slightly altered within subsequent recordings. Seven different signers are employed to perform 5 individual and commonly met scenarios in different public services. The average length of each scenario is twenty sentences.\n\nThe dataset contains 10,290 sentence instances, 40,785 gloss instances, 310 unique glosses (vocabulary size) and 331 unique sentences, with 4.23 glosses per sentence on average. Each signer is asked to perform the pre-defined dialogues five consecutive times. In all cases, the simulation considers a deaf person communicating with a single public service employee. The involved signer performs the sequence of glosses of both agents in the discussion. For the annotation of each gloss sequence, GSL linguistic experts are involved. The given annotations are at individual gloss and gloss sequence level. A translation of the gloss sentences to spoken Greek is also provided.\n\nEvaluation\nThe GSL dataset includes the 3 evaluation setups:\n\n\n\nSigner-dependent continuous sign language recognition (GSL SD) – roughly 80% of videos are used for training, corresponding to 8,189 instances. The rest 1,063 (10%) were kept for validation and 1,043 (10%) for testing.\n\n\n\nSigner-independent continuous sign language recognition (GSL SI) – the selected test gloss sequences are not used in the training set, while all the individual glosses exist in the training set. In GSL SI, the recordings of one signer are left out for validation and testing (588 and 881 instances, respectively). The rest 8821 instances are utilized for training.\n\n\n\nIsolated gloss sign language recognition (GSL isol.) – The validation set consists of 2,231 gloss instances, the test set 3,500, while the remaining 34,995 are used for training. All 310 unique glosses are seen in the training set.\n\n\n\nFor more info and results, advice our paper\n\nPaper Abstract: A Comprehensive Study on Sign Language Recognition Methods, Adaloglou et al. 2020\nIn this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted. By implementing the most recent deep neural network methods in this field, a thorough evaluation on multiple publicly available datasets is performed. The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses. For this task, two new sequence training criteria, known from the fields of speech and scene text recognition, are introduced. Furthermore, a\nplethora of pretraining schemes are thoroughly discussed. Finally, a new RGB+D dataset for the Greek sign language is created. To the best of our knowledge, this is the first sign language dataset where sentence and gloss level annotations are provided for every video capture.\n\nArxiv link" }, { "dkey": "ASSET Corpus", "dval": "A crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations." }, { "dkey": "TextComplexityDE", "dval": "TextComplexityDE is a dataset consisting of 1000 sentences in German language taken from 23 Wikipedia articles in 3 different article-genres to be used for developing text-complexity predictor models and automatic text simplification in German language. The dataset includes subjective assessment of different text-complexity aspects provided by German learners in level A and B. In addition, it contains manual simplification of 250 of those sentences provided by native speakers and subjective assessment of the simplified sentences by participants from the target group. The subjective ratings were collected using both laboratory studies and crowdsourcing approach." }, { "dkey": "VIST", "dval": "The Visual Storytelling Dataset (VIST) consists of 210,819 unique photos and 50,000 stories. The images were collected from albums on Flickr. The albums included 10 to 50 images and all the images in an album are taken in a 48-hour span. The stories were created by workers on Amazon Mechanical Turk, where the workers were instructed to choose five images from the album and write a story about them. Every story has five sentences, and every sentence is paired with its appropriate image. The dataset is split into 3 subsets, a training set (80%), a validation set (10%) and a test set (10%). All the words and interpunction signs in the stories are separated by a space character and all the location names are replaced with the word location. All the names of people are replaced with the words male or female depending on the gender of the person." } ]

[ "LSP", "PoseTrack", "V-COCO", "K2HPD", "UMDFaces", "MuPoTS-3D" ]

[ "COCO", "MPII" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "MPII", "dval": "The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 different human activities and the poses are manually annotated with up to 16 body joints." }, { "dkey": "LSP", "dval": "The Leeds Sports Pose (LSP) dataset is widely used as the benchmark for human pose estimation. The original LSP dataset contains 2,000 images of sportspersons gathered from Flickr, 1000 for training and 1000 for testing. Each image is annotated with 14 joint locations, where left and right joints are consistently labelled from a person-centric viewpoint. The extended LSP dataset contains additional 10,000 images labeled for training.\n\nImage: Sumer et al" }, { "dkey": "PoseTrack", "dval": "The PoseTrack dataset is a large-scale benchmark for multi-person pose estimation and tracking in videos. It requires not only pose estimation in single frames, but also temporal tracking across frames. It contains 514 videos including 66,374 frames in total, split into 300, 50 and 208 videos for training, validation and test set respectively. For training videos, 30 frames from the center are annotated. For validation and test videos, besides 30 frames from the center, every fourth frame is also annotated for evaluating long range articulated tracking. The annotations include 15 body keypoints location, a unique person id and a head bounding box for each person instance." }, { "dkey": "V-COCO", "dval": "Verbs in COCO (V-COCO) is a dataset that builds off COCO for human-object interaction detection. V-COCO provides 10,346 images (2,533 for training, 2,867 for validating and 4,946 for testing) and 16,199 person instances. Each person has annotations for 29 action categories and there are no interaction labels including objects." }, { "dkey": "K2HPD", "dval": "Includes 100K depth images under challenging scenarios." }, { "dkey": "UMDFaces", "dval": "UMDFaces is a face dataset divided into two parts:\n\n\nStill Images - 367,888 face annotations for 8,277 subjects.\nVideo Frames - Over 3.7 million annotated video frames from over 22,000 videos of 3100 subjects.\n\nPart 1 - Still Images\n\nThe dataset contains 367,888 face annotations for 8,277 subjects divided into 3 batches. The annotations contain human curated bounding boxes for faces and estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network.\n\nPart 2 - Video Frames\n\nThe second part contains 3,735,476 annotated video frames extracted from a total of 22,075 for 3,107 subjects. The annotations contain the estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network." }, { "dkey": "MuPoTS-3D", "dval": "MuPoTs-3D (Multi-person Pose estimation Test Set in 3D) is a dataset for pose estimation composed of more than 8,000 frames from 20 real-world scenes with up to three subjects. The poses are annotated with a 14-point skeleton model." } ]

[ "SNIPS", "Visual Madlibs", "PAQ", "VATEX", "CLUECorpus2020", "Chart2Text", "DiDeMo" ]

[ "Flickr30k", "COCO" ]

[ { "dkey": "Flickr30k", "dval": "The Flickr30k dataset contains 31,000 images collected from Flickr, together with 5 reference sentences provided by human annotators." }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "Visual Madlibs", "dval": "Visual Madlibs is a dataset consisting of 360,001 focused natural language descriptions for 10,738 images. This dataset is collected using automatically produced fill-in-the-blank templates designed to gather targeted descriptions about: people and objects, their appearances, activities, and interactions, as well as inferences about the general scene or its broader context." }, { "dkey": "PAQ", "dval": "Probably Asked Questions (PAQ) is a very large resource of 65M automatically-generated QA-pairs. PAQ is a semi-structured Knowledge Base (KB) of 65M natural language QA-pairs, which models can memorise and/or learn to retrieve from. PAQ differs from traditional KBs in that questions and answers are stored in natural language, and that questions are generated such that they are likely to appear in ODQA datasets. PAQ is automatically constructed using a question generation model and Wikipedia." }, { "dkey": "VATEX", "dval": "VATEX is multilingual, large, linguistically complex, and diverse dataset in terms of both video and natural language descriptions. It has two tasks for video-and-language research: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context." }, { "dkey": "CLUECorpus2020", "dval": "CLUECorpus2020 is a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language generation. It has 100G raw corpus with 35 billion Chinese characters, which is retrieved from Common Crawl." }, { "dkey": "Chart2Text", "dval": "Chart2Text is a dataset that was crawled from 23,382 freely accessible pages from statista.com in early March of 2020, yielding a total of 8,305 charts, and associated summaries. For each chart, the chart image, the underlying data table, the title, the axis labels, and a human-written summary describing the statistic was downloaded." }, { "dkey": "DiDeMo", "dval": "The Distinct Describable Moments (DiDeMo) dataset is one of the largest and most diverse datasets for the temporal localization of events in videos given natural language descriptions. The videos are collected from Flickr and each video is trimmed to a maximum of 30 seconds. The videos in the dataset are divided into 5-second segments to reduce the complexity of annotation. The dataset is split into training, validation and test sets containing 8,395, 1,065 and 1,004 videos respectively. The dataset contains a total of 26,892 moments and one moment could be associated with descriptions from multiple annotators. The descriptions in DiDeMo dataset are detailed and contain camera movement, temporal transition indicators, and activities. Moreover, the descriptions in DiDeMo are verified so that each description refers to a single moment." } ]

[ "THEODORE", "COVIDx", "UNITOPATHO", "GoPro" ]

[ "MPII", "CIFAR-10" ]

[ { "dkey": "MPII", "dval": "The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 different human activities and the poses are manually annotated with up to 16 body joints." }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "THEODORE", "dval": "Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset." }, { "dkey": "COVIDx", "dval": "An open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge." }, { "dkey": "UNITOPATHO", "dval": "Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin stained patches extracted from 292 whole-slide images, meant for training deep neural networks for colorectal polyps classification and adenomas grading. The slides are acquired through a Hamamatsu Nanozoomer S210 scanner at 20× magnification (0.4415 μm/px)" }, { "dkey": "GoPro", "dval": "The GoPro dataset for deblurring consists of 3,214 blurred images with the size of 1,280×720 that are divided into 2,103 training images and 1,111 test images. The dataset consists of pairs of a realistic blurry image and the corresponding ground truth shapr image that are obtained by a high-speed camera." } ]

[ "Chinese Classifier", "MTL-AQA", "ROSTD", "FaceForensics", "UCF101", "FoodX-251" ]

[ "NABirds", "ImageNet" ]

[ { "dkey": "NABirds", "dval": "NABirds V1 is a collection of 48,000 annotated photographs of the 400 species of birds that are commonly observed in North America. More than 100 photographs are available for each species, including separate annotations for males, females and juveniles that comprise 700 visual categories. This dataset is to be used for fine-grained visual categorization experiments." }, { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "Chinese Classifier", "dval": "Classifiers are function words that are used to express quantities in Chinese and are especially difficult for language learners. This dataset of Chinese Classifiers can be used to predict Chinese classifiers from context.\nThe dataset contains a large collection of example sentences for Chinese classifier usage derived from three language corpora (Lancaster Corpus of Mandarin Chinese, UCLA Corpus of Written Chinese and Leiden Weibo Corpus). The data was cleaned and processed for a context-based classifier prediction task." }, { "dkey": "MTL-AQA", "dval": "A new multitask action quality assessment (AQA) dataset, the largest to date, comprising of more than 1600 diving samples; contains detailed annotations for fine-grained action recognition, commentary generation, and estimating the AQA score. Videos from multiple angles provided wherever available." }, { "dkey": "ROSTD", "dval": "A dataset of 4K out-of-domain (OOD) examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, the examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset." }, { "dkey": "FaceForensics", "dval": "FaceForensics is a video dataset consisting of more than 500,000 frames containing faces from 1004 videos that can be used to study image or video forgeries. All videos are downloaded from Youtube and are cut down to short continuous clips that contain mostly frontal faces. This dataset has two versions:\n\n\n\nSource-to-Target: where the authors reenact over 1000 videos with new facial expressions extracted from other videos, which e.g. can be used to train a classifier to detect fake images or videos.\n\n\n\nSelfreenactment: where the authors use Face2Face to reenact the facial expressions of videos with their own facial expressions as input to get pairs of videos, which e.g. can be used to train supervised generative refinement models." }, { "dkey": "UCF101", "dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240." }, { "dkey": "FoodX-251", "dval": "FoodX-251 is a dataset of 251 fine-grained classes with 118k training, 12k validation and 28k test images. Human verified labels are made available for the training and test images. The classes are fine-grained and visually similar, for example, different types of cakes, sandwiches, puddings, soups, and pastas." } ]

[ "MSR ActionPairs", "KITTI-trajectory-prediction", "TrajNet", "HiEve", "TUM-GAID" ]

[ "UCF101", "HMDB51" ]

[ { "dkey": "UCF101", "dval": "UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240." }, { "dkey": "HMDB51", "dval": "The HMDB51 dataset is a large collection of realistic videos from various sources, including movies and web videos. The dataset is composed of 6,766 video clips from 51 action categories (such as “jump”, “kiss” and “laugh”), with each category containing at least 101 clips. The original evaluation scheme uses three different training/testing splits. In each split, each action class has 70 clips for training and 30 clips for testing. The average accuracy over these three splits is used to measure the final performance." }, { "dkey": "MSR ActionPairs", "dval": "This is a 3D action recognition dataset, also known as 3D Action Pairs dataset. The actions in this dataset are selected in pairs such that the two actions of each pair are similar in motion (have similar trajectories) and shape (have similar objects); however, the motion-shape relation is different." }, { "dkey": "KITTI-trajectory-prediction", "dval": "KITTI is a well established dataset in the computer vision community. It has often been used for trajectory prediction despite not having a well defined split, generating non comparable baselines in different works. This dataset aims at bridging this gap and proposes a well defined split of the KITTI data.\nSamples are collected as 6 seconds chunks (2seconds for past and 4 for future) in a sliding window fashion from all trajectories in the dataset, including the egovehicle. There are a total of 8613 top-view trajectories for training and 2907 for testing.\nSince top-view maps are not provided by KITTI, semantic labels of static categories obtained with DeepLab-v3+ from all frames are projected in a common top-view map using the Velodyne 3D point cloud and IMU. The resulting maps have a spatial resolution of 0.5 meters and are provided along with the trajectories." }, { "dkey": "TrajNet", "dval": "The TrajNet Challenge represents a large multi-scenario forecasting benchmark. The challenge consists on predicting 3161 human trajectories, observing for each trajectory 8 consecutive ground-truth values (3.2 seconds) i.e., t−7,t−6,…,t, in world plane coordinates (the so-called world plane Human-Human protocol) and forecasting the following 12 (4.8 seconds), i.e., t+1,…,t+12. The 8-12-value protocol is consistent with the most trajectory forecasting approaches, usually focused on the 5-dataset ETH-univ + ETH-hotel + UCY-zara01 + UCY-zara02 + UCY-univ. Trajnet extends substantially the 5-dataset scenario by diversifying the training data, thus stressing the flexibility and generalization one approach has to exhibit when it comes to unseen scenery/situations. In fact, TrajNet is a superset of diverse datasets that requires to train on four families of trajectories, namely 1) BIWI Hotel (orthogonal bird’s eye flight view, moving people), 2) Crowds UCY (3 datasets, tilted bird’s eye view, camera mounted on building or utility poles, moving people), 3) MOT PETS (multisensor, different human activities) and 4) Stanford Drone Dataset (8 scenes, high orthogonal bird’s eye flight view, different agents as people, cars etc. ), for a total of 11448 trajectories. Testing is requested on diverse partitions of BIWI Hotel, Crowds UCY, Stanford Drone Dataset, and is evaluated by a specific server (ground-truth testing data is unavailable for applicants)." }, { "dkey": "HiEve", "dval": "A new large-scale dataset for understanding human motions, poses, and actions in a variety of realistic events, especially crowd & complex events. It contains a record number of poses (>1M), the largest number of action labels (>56k) for complex events, and one of the largest number of trajectories lasting for long terms (with average trajectory length >480). Besides, an online evaluation server is built for researchers to evaluate their approaches." }, { "dkey": "TUM-GAID", "dval": "TUM-GAID (TUM Gait from Audio, Image and Depth) collects 305 subjects performing two walking trajectories in an indoor environment. The first trajectory is traversed from left to right and the second one from right to left. Two recording sessions were performed, one in January, where subjects wore heavy jackets and mostly winter boots, and another one in April, where subjects wore lighter clothes. The action is captured by a Microsoft Kinect sensor which provides a video stream with a resolution of 640×480 pixels and a frame rate around 30 FPS." } ]

[ "CUB-200-2011", "CLEVR", "PA-HMDB51", "CompCars" ]

[ "Market-1501", "CUHK03" ]

[ { "dkey": "Market-1501", "dval": "Market-1501 is a large-scale public benchmark dataset for person re-identification. It contains 1501 identities which are captured by six different cameras, and 32,668 pedestrian image bounding-boxes obtained using the Deformable Part Models pedestrian detector. Each person has 3.6 images on average at each viewpoint. The dataset is split into two parts: 750 identities are utilized for training and the remaining 751 identities are used for testing. In the official testing protocol 3,368 query images are selected as probe set to find the correct match across 19,732 reference gallery images." }, { "dkey": "CUHK03", "dval": "The CUHK03 consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector. The dataset also provides 20 random train/test splits in which 100 identities are selected for testing and the rest for training" }, { "dkey": "CUB-200-2011", "dval": "The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions." }, { "dkey": "CLEVR", "dval": "CLEVR (Compositional Language and Elementary Visual Reasoning) is a synthetic Visual Question Answering dataset. It contains images of 3D-rendered objects; each image comes with a number of highly compositional questions that fall into different categories. Those categories fall into 5 classes of tasks: Exist, Count, Compare Integer, Query Attribute and Compare Attribute. The CLEVR dataset consists of: a training set of 70k images and 700k questions, a validation set of 15k images and 150k questions, A test set of 15k images and 150k questions about objects, answers, scene graphs and functional programs for all train and validation images and questions. Each object present in the scene, aside of position, is characterized by a set of four attributes: 2 sizes: large, small, 3 shapes: square, cylinder, sphere, 2 material types: rubber, metal, 8 color types: gray, blue, brown, yellow, red, green, purple, cyan, resulting in 96 unique combinations." }, { "dkey": "PA-HMDB51", "dval": "The Privacy Annotated HMDB51 (PA-HMDB51) dataset is a video-based dataset for evaluating pirvacy protection in visual action recognition algorithms. The dataset contains both target task labels (action) and selected privacy attributes (skin color, face, gender, nudity, and relationship) annotated on a per-frame basis." }, { "dkey": "CompCars", "dval": "The Comprehensive Cars (CompCars) dataset contains data from two scenarios, including images from web-nature and surveillance-nature. The web-nature data contains 163 car makes with 1,716 car models. There are a total of 136,726 images capturing the entire cars and 27,618 images capturing the car parts. The full car images are labeled with bounding boxes and viewpoints. Each car model is labeled with five attributes, including maximum speed, displacement, number of doors, number of seats, and type of car. The surveillance-nature data contains 50,000 car images captured in the front view. \n\nThe dataset can be used for the tasks of:\n\n\nFine-grained classification\nAttribute prediction\nCar model verification\n\nThe dataset can be also used for other tasks such as image ranking, multi-task learning, and 3D reconstruction." } ]

[ "Localized Narratives", "Replica", "A2D", "Cityscapes" ]

[ "ScanNet", "SceneNN" ]

[ { "dkey": "ScanNet", "dval": "ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects. Up to now, ScanNet v2, the newest version of ScanNet, has collected 1513 annotated scans with an approximate 90% surface coverage. In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects." }, { "dkey": "SceneNN", "dval": "SceneNN is an RGB-D scene dataset consisting of more than 100 indoor scenes. The scenes are captured at various places, e.g., offices, dormitory, classrooms, pantry, etc., from University of Massachusetts Boston and Singapore University of Technology and Design.\nAll scenes are reconstructed into triangle meshes and have per-vertex and per-pixel annotation. The dataset is additionally enriched with fine-grained information such as axis-aligned bounding boxes, oriented bounding boxes, and object poses." }, { "dkey": "Localized Narratives", "dval": "We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing. Since the voice and the mouse pointer are synchronized, we can localize every single word in the description. This dense visual grounding takes the form of a mouse trace segment per word and is unique to our data. We annotated 849k images with Localized Narratives: the whole COCO, Flickr30k, and ADE20K datasets, and 671k images of Open Images, all of which we make publicly available. We provide an extensive analysis of these annotations showing they are diverse, accurate, and efficient to produce. We also demonstrate their utility on the application of controlled image captioning." }, { "dkey": "Replica", "dval": "The Replica Dataset is a dataset of high quality reconstructions of a variety of indoor spaces. Each reconstruction has clean dense geometry, high resolution and high dynamic range textures, glass and mirror surface information, planar segmentation as well as semantic class and instance segmentation." }, { "dkey": "A2D", "dval": "A2D (Actor-Action Dataset) is a dataset for simultaneously inferring actors and actions in videos. A2D has seven actor classes (adult, baby, ball, bird, car, cat, and dog) and eight action classes (climb, crawl, eat, fly, jump, roll, run, and walk) not including the no-action class, which we also consider. The A2D has 3,782 videos with at least 99 instances per valid actor-action tuple and videos are labeled with both pixel-level actors and actions for sampled frames. The A2D dataset serves as a large-scale testbed for various vision problems: video-level single- and multiple-label actor-action recognition, instance-level object segmentation/co-segmentation, as well as pixel-level actor-action semantic segmentation to name a few." }, { "dkey": "Cityscapes", "dval": "Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. Data was captured in 50 cities during several months, daytimes, and good weather conditions. It was originally recorded as video so the frames were manually selected to have the following features: large number of dynamic objects, varying scene layout, and varying background." } ]

[ "TweetEval", "MovieShots", "TSU", "AMASS", "Drive&Act" ]

[ "Breakfast", "Charades" ]

[ { "dkey": "Breakfast", "dval": "The Breakfast Actions Dataset comprises of 10 actions related to breakfast preparation, performed by 52 different individuals in 18 different kitchens. The dataset is one of the largest fully annotated datasets available. The actions are recorded “in the wild” as opposed to a single controlled lab environment. It consists of over 77 hours of video recordings." }, { "dkey": "Charades", "dval": "The Charades dataset is composed of 9,848 videos of daily indoors activities with an average length of 30 seconds, involving interactions with 46 objects classes in 15 types of indoor scenes and containing a vocabulary of 30 verbs leading to 157 action classes. Each video in this dataset is annotated by multiple free-text descriptions, action labels, action intervals and classes of interacting objects. 267 different users were presented with a sentence, which includes objects and actions from a fixed vocabulary, and they recorded a video acting out the sentence. In total, the dataset contains 66,500 temporal annotations for 157 action classes, 41,104 labels for 46 object classes, and 27,847 textual descriptions of the videos. In the standard split there are7,986 training video and 1,863 validation video." }, { "dkey": "TweetEval", "dval": "TweetEval introduces an evaluation framework consisting of seven heterogeneous Twitter-specific classification tasks." }, { "dkey": "MovieShots", "dval": "MovieShots is a dataset to facilitate the shot type analysis in videos. It is a large-scale shot type annotation set that contains 46K shots from 7,858 movies covering a wide\nvariety of movie genres to ensure the inclusion of all scale and movement types of shot. Each shot has two attributes, shot scale and shot movement.\n\nShot scale has five categories: 1) long shot (LS) is taken from a long distance, sometimes as far as a quarter of a mile away; 2) full shot (FS) barely includes the human body in full; 3) medium shot (MS) contains a figure from the knees or waist up; 4) close-up shot (CS) concentrates on a relatively small object, showing the face of the hand of a person; (5) extreme close-up shot (ECS) shows even smaller parts such as the image of an eye or a mouth.\n\nShot movement has four categories: 1) in static shot, the camera is fixed but the subject is flexible to move; 2) for motion shot, the camera moves or rotates; 3) the camera zooms in for push shot, and 4) zooms out for pull shot. While all the four movement types are widely used in movies, the use of push and pull shots only takes a very small portion. The usage of different shots usually depends on the movie genres and the preferences of the filmmakers." }, { "dkey": "TSU", "dval": "Toyota Smarthome Untrimmed (TSU) is a dataset for activity detection in long untrimmed videos. The dataset contains 536 videos with an average duration of 21 mins. Since this dataset is based on the same footage video as Toyota Smarthome Trimmed version, it features the same challenges and introduces additional ones. The dataset is annotated with 51 activities.\n\nThe dataset has been recorded in an apartment equipped with 7 Kinect v1 cameras. It contains common daily living activities of 18 subjects. The subjects are senior people in the age range 60-80 years old. The dataset has a resolution of 640×480 and offers 3 modalities: RGB + Depth + 3D Skeleton. The 3D skeleton joints were extracted from RGB. For privacy-preserving reasons, the face of the subjects is blurred." }, { "dkey": "AMASS", "dval": "AMASS is a large database of human motion unifying different optical marker-based motion capture datasets by representing them within a common framework and parameterization. AMASS is readily useful for animation, visualization, and generating training data for deep learning." }, { "dkey": "Drive&Act", "dval": "The Drive&Act dataset is a state of the art multi modal benchmark for driver behavior recognition. The dataset includes 3D skeletons in addition to frame-wise hierarchical labels of 9.6 Million frames captured by 6 different views and 3 modalities (RGB, IR and depth).\n\nIt offers following key features:\n\n\n12h of video data in 29 long sequences\nCalibrated multi view camera system with 5 views\nMulti modal videos: NIR, Depth and Color data\nMarkerless motion capture: 3D Body Pose and Head Pose\nModel of the static interior of the car\n83 manually annotated hierarchical activity labels:\nLevel 1: Long running tasks (12)\nLevel 2: Semantic actions (34)\nLevel 3: Object Interaction tripplets [action|object|location] (6|17|14)" } ]

[ "InterHand2.6M", "MuCo-3DHP", "3D Hand Pose", "SynthHands", "FAT", "MuPoTS-3D" ]

[ "MPII", "COCO", "FreiHAND" ]

[ { "dkey": "MPII", "dval": "The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 different human activities and the poses are manually annotated with up to 16 body joints." }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "FreiHAND", "dval": "FreiHAND is a 3D hand pose dataset which records different hand actions performed by 32 people. For each hand image, MANO-based 3D hand pose annotations are provided. It currently contains 32,560 unique training samples and 3960 unique samples for evaluation. The training samples are recorded with a green screen background allowing for background removal. In addition, it applies three different post processing strategies to training samples for data augmentation. However, these post processing strategies are not applied to evaluation samples." }, { "dkey": "InterHand2.6M", "dval": "The InterHand2.6M dataset is a large-scale real-captured dataset with accurate GT 3D interacting hand poses, used for 3D hand pose estimation The dataset contains 2.6M labeled single and interacting hand frames." }, { "dkey": "MuCo-3DHP", "dval": "MuCo-3DHP is a large scale training data set showing real images of sophisticated multi-person interactions and occlusions." }, { "dkey": "3D Hand Pose", "dval": "3D Hand Pose is a multi-view hand pose dataset consisting of color images of hands and different kind of annotations for each: the bounding box and the 2D and 3D location on the joints in the hand." }, { "dkey": "SynthHands", "dval": "The SynthHands dataset is a dataset for hand pose estimation which consists of real captured hand motion retargeted to a virtual hand with natural backgrounds and interactions with different objects. The dataset contains data for male and female hands, both with and without interaction with objects. While the hand and foreground object are synthtically generated using Unity, the motion was obtained from real performances as described in the accompanying paper. In addition, real object textures and background images (depth and color) were used. Ground truth 3D positions are provided for 21 keypoints of the hand." }, { "dkey": "FAT", "dval": "Falling Things (FAT) is a dataset for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics. It consists of generated photorealistic images with accurate 3D pose annotations for all objects in 60k images.\n\nThe 60k annotated photos of 21 household objects are taken from the YCB objects set. For each image, the dataset contains the 3D poses, per-pixel class segmentation, and 2D/3D bounding box coordinates for all objects." }, { "dkey": "MuPoTS-3D", "dval": "MuPoTs-3D (Multi-person Pose estimation Test Set in 3D) is a dataset for pose estimation composed of more than 8,000 frames from 20 real-world scenes with up to three subjects. The poses are annotated with a 14-point skeleton model." } ]

[ "SCDB", "CovidQA", "Tiny ImageNet", "PUBHEALTH", "QuaRel", "SciTail" ]

[ "ImageNet", "Adience" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "Adience", "dval": "The Adience dataset, published in 2014, contains 26,580 photos across 2,284 subjects with a binary gender label and one label from eight different age groups, partitioned into five splits. The key principle of the data set is to capture the images as close to real world conditions as possible, including all variations in appearance, pose, lighting condition and image quality, to name a few." }, { "dkey": "SCDB", "dval": "Includes annotations for 10 distinguishable concepts." }, { "dkey": "CovidQA", "dval": "The beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge." }, { "dkey": "Tiny ImageNet", "dval": "Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images and 50 test images." }, { "dkey": "PUBHEALTH", "dval": "PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label." }, { "dkey": "QuaRel", "dval": "QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms." }, { "dkey": "SciTail", "dval": "The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples with neutral label." } ]

[ "THEODORE", "PA-100K", "SemArt", "LAD" ]

[ "ImageNet", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "THEODORE", "dval": "Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset." }, { "dkey": "PA-100K", "dval": "PA-100K is a recent-proposed large pedestrian attribute dataset, with 100,000 images in total collected from outdoor surveillance cameras. It is split into 80,000 images for the training set, and 10,000 for the validation set and 10,000 for the test set. This dataset is labeled by 26 binary attributes. The common features existing in both selected dataset is that the images are blurry due to the relatively low resolution and the positive ratio of each binary attribute is low." }, { "dkey": "SemArt", "dval": "SemArt is a multi-modal dataset for semantic art understanding. SemArt is a collection of fine-art painting images in which each image is associated to a number of attributes and a textual artistic comment, such as those that appear in art catalogues or museum collections. It contains 21,384 samples that provides artistic comments along with fine-art paintings and their attributes for studying semantic art understanding." }, { "dkey": "LAD", "dval": "LAD (Large-scale Attribute Dataset) has 78,017 images of 5 super-classes and 230 classes. The image number of LAD is larger than the sum of the four most popular attribute datasets (AwA, CUB, aP/aY and SUN). 359 attributes of visual, semantic and subjective properties are defined and annotated in instance-level." } ]

[ "MVSEC", "SlowFlow", "Virtual KITTI", "CrowdFlow", "Creative Flow+ Dataset", "EDEN", "FG-NET" ]

[ "Middlebury", "KITTI" ]

[ { "dkey": "Middlebury", "dval": "The Middlebury Stereo dataset consists of high-resolution stereo sequences with complex geometry and pixel-accurate ground-truth disparity data. The ground-truth disparities are acquired using a novel technique that employs structured lighting and does not require the calibration of the light projectors." }, { "dkey": "KITTI", "dval": "KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist." }, { "dkey": "MVSEC", "dval": "The Multi Vehicle Stereo Event Camera (MVSEC) dataset is a collection of data designed for the development of novel 3D perception algorithms for event based cameras. Stereo event data is collected from car, motorbike, hexacopter and handheld data, and fused with lidar, IMU, motion capture and GPS to provide ground truth pose and depth images." }, { "dkey": "SlowFlow", "dval": "SlowFlow is an optical flow dataset collected by applying Slow Flow technique on data from a high-speed camera and analyzing the performance of the state-of-the-art in optical flow under various levels of motion blur." }, { "dkey": "Virtual KITTI", "dval": "Virtual KITTI is a photo-realistic synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation, optical flow, and depth estimation.\n\nVirtual KITTI contains 50 high-resolution monocular videos (21,260 frames) generated from five different virtual worlds in urban settings under different imaging and weather conditions. These worlds were created using the Unity game engine and a novel real-to-virtual cloning method. These photo-realistic synthetic videos are automatically, exactly, and fully annotated for 2D and 3D multi-object tracking and at the pixel level with category, instance, flow, and depth labels (cf. below for download links)." }, { "dkey": "CrowdFlow", "dval": "The TUB CrowdFlow is a synthetic dataset that contains 10 sequences showing 5 scenes. Each scene is rendered twice: with a static point of view and a dynamic camera to simulate drone/UAV based surveillance. The scenes are render using Unreal Engine at HD resolution (1280x720) at 25 fps, which is typical for current commercial CCTV surveillance systems. The total number of frames is 3200.\n\nEach sequence has the following ground-truth data:\n\n\nOptical flow fields\nPerson trajectories (up to 1451)\nDense pixel trajectories" }, { "dkey": "Creative Flow+ Dataset", "dval": "Includes 3000 animated sequences rendered using styles randomly selected from 40 textured line styles and 38 shading styles, spanning the range between flat cartoon fill and wildly sketchy shading. The dataset includes 124K+ train set frames and 10K test set frames rendered at 1500x1500 resolution, far surpassing the largest available optical flow datasets in size." }, { "dkey": "EDEN", "dval": "EDEN (Enclosed garDEN) is a multimodal synthetic dataset, a dataset for nature-oriented applications. The dataset features more than 300K images captured from more than 100 garden models. Each image is annotated with various low/high-level vision modalities, including semantic segmentation, depth, surface normals, intrinsic colors, and optical flow." }, { "dkey": "FG-NET", "dval": "FGNet is a dataset for age estimation and face recognition across ages. It is composed of a total of 1,002 images of 82 people with age range from 0 to 69 and an age gap up to 45 years" } ]

[ "FaceForensics", "JAFFE", "Oulu-CASIA", "SFEW", "BP4D", "FRGC" ]

[ "RaFD", "CelebA" ]

[ { "dkey": "RaFD", "dval": "The Radboud Faces Database (RaFD) is a set of pictures of 67 models (both adult and children, males and females) displaying 8 emotional expressions." }, { "dkey": "CelebA", "dval": "CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age." }, { "dkey": "FaceForensics", "dval": "FaceForensics is a video dataset consisting of more than 500,000 frames containing faces from 1004 videos that can be used to study image or video forgeries. All videos are downloaded from Youtube and are cut down to short continuous clips that contain mostly frontal faces. This dataset has two versions:\n\n\n\nSource-to-Target: where the authors reenact over 1000 videos with new facial expressions extracted from other videos, which e.g. can be used to train a classifier to detect fake images or videos.\n\n\n\nSelfreenactment: where the authors use Face2Face to reenact the facial expressions of videos with their own facial expressions as input to get pairs of videos, which e.g. can be used to train supervised generative refinement models." }, { "dkey": "JAFFE", "dval": "The JAFFE dataset consists of 213 images of different facial expressions from 10 different Japanese female subjects. Each subject was asked to do 7 facial expressions (6 basic facial expressions and neutral) and the images were annotated with average semantic ratings on each facial expression by 60 annotators." }, { "dkey": "Oulu-CASIA", "dval": "The Oulu-CASIA NIR&VIS facial expression database consists of six expressions (surprise, happiness, sadness, anger, fear and disgust) from 80 people between 23 and 58 years old. 73.8% of the subjects are males. The subjects were asked to sit on a chair in the observation room in a way that he/ she is in front of camera. Camera-face distance is about 60 cm. Subjects were asked to make a facial expression according to an expression example shown in picture sequences. The imaging hardware works at the rate of 25 frames per second and the image resolution is 320 × 240 pixels." }, { "dkey": "SFEW", "dval": "The Static Facial Expressions in the Wild (SFEW) dataset is a dataset for facial expression recognition. It was created by selecting static frames from the AFEW database by computing key frames based on facial point clustering. The most commonly used version, SFEW 2.0, was the benchmarking data for the SReco sub-challenge in EmotiW 2015. SFEW 2.0 has been divided into three sets: Train (958 samples), Val (436 samples) and Test (372 samples). Each of the images is assigned to one of seven expression categories, i.e., anger, disgust, fear, neutral, happiness, sadness, and surprise. The expression labels of the training and validation sets are publicly available, whereas those of the testing set are held back by the challenge organizer." }, { "dkey": "BP4D", "dval": "The BP4D-Spontaneous dataset is a 3D video database of spontaneous facial expressions in a diverse group of young adults. Well-validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication. Frame-level ground-truth for facial actions was obtained using the Facial Action Coding System. Facial features were tracked in both 2D and 3D domains using both person-specific and generic approaches.\nThe database includes forty-one participants (23 women, 18 men). They were 18 – 29 years of age; 11 were Asian, 6 were African-American, 4 were Hispanic, and 20 were Euro-American. An emotion elicitation protocol was designed to elicit emotions of participants effectively. Eight tasks were covered with an interview process and a series of activities to elicit eight emotions.\nThe database is structured by participants. Each participant is associated with 8 tasks. For each task, there are both 3D and 2D videos. As well, the Metadata include manually annotated action units (FACS AU), automatically tracked head pose, and 2D/3D facial landmarks. The database is in the size of about 2.6TB (without compression)." }, { "dkey": "FRGC", "dval": "The data for FRGC consists of 50,000 recordings divided into training and validation partitions. The training partition is designed for training algorithms and the validation partition is for assessing performance of an approach in a laboratory setting. The validation partition consists of data from 4,003 subject sessions. A subject session is the set of all images of a person taken each time a person's biometric data is collected and consists of four controlled still images, two uncontrolled still images, and one three-dimensional image. The controlled images were taken in a studio setting, are full frontal facial images taken under two lighting conditions and with two facial expressions (smiling and neutral). The uncontrolled images were taken in varying illumination conditions; e.g., hallways, atriums, or outside. Each set of uncontrolled images contains two expressions, smiling and neutral. The 3D image was taken under controlled illumination conditions. The 3D images consist of both a range and a texture image. The 3D images were acquired by a Minolta Vivid 900/910 series sensor." } ]

[ "DiDeMo", "SUN09", "LAReQA", "Fashion IQ", "Violin" ]

[ "Flickr30k", "COCO" ]

[ { "dkey": "Flickr30k", "dval": "The Flickr30k dataset contains 31,000 images collected from Flickr, together with 5 reference sentences provided by human annotators." }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "DiDeMo", "dval": "The Distinct Describable Moments (DiDeMo) dataset is one of the largest and most diverse datasets for the temporal localization of events in videos given natural language descriptions. The videos are collected from Flickr and each video is trimmed to a maximum of 30 seconds. The videos in the dataset are divided into 5-second segments to reduce the complexity of annotation. The dataset is split into training, validation and test sets containing 8,395, 1,065 and 1,004 videos respectively. The dataset contains a total of 26,892 moments and one moment could be associated with descriptions from multiple annotators. The descriptions in DiDeMo dataset are detailed and contain camera movement, temporal transition indicators, and activities. Moreover, the descriptions in DiDeMo are verified so that each description refers to a single moment." }, { "dkey": "SUN09", "dval": "The SUN09 dataset consists of 12,000 annotated images with more than 200 object categories. It consists of natural, indoor and outdoor images. Each image contains an average of 7 different annotated objects and the average occupancy of each object is 5% of image size. The frequencies of object categories follow a power law distribution." }, { "dkey": "LAReQA", "dval": "A challenging new benchmark for language-agnostic answer retrieval from a multilingual candidate pool." }, { "dkey": "Fashion IQ", "dval": "Fashion IQ support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashion dataset to provide human-generated captions that distinguish similar pairs of garment images together with side-information consisting of real-world product descriptions and derived visual attribute labels for these images." }, { "dkey": "Violin", "dval": "Video-and-Language Inference is the task of joint multimodal understanding of video and text. Given a video clip with aligned subtitles as premise, paired with a natural language hypothesis based on the video content, a model needs to infer whether the hypothesis is entailed or contradicted by the given video clip. The Violin dataset is a dataset for this task which consists of 95,322 video-hypothesis pairs from 15,887 video clips, spanning over 582 hours of video. These video clips contain rich content with diverse temporal dynamics, event shifts, and people interactions, collected from two sources: (i) popular TV shows, and (ii) movie clips from YouTube channels." } ]

[ "Paris-Lille-3D", "CCAligned", "Opusparcus", "Fashionpedia", "SNIPS" ]

[ "DBpedia", "YAGO" ]

[ { "dkey": "DBpedia", "dval": "DBpedia (from \"DB\" for \"database\") is a project aiming to extract structured content from the information created in the Wikipedia project. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets." }, { "dkey": "YAGO", "dval": "Yet Another Great Ontology (YAGO) is a Knowledge Graph that augments WordNet with common knowledge facts extracted from Wikipedia, converting WordNet from a primarily linguistic resource to a common knowledge base. YAGO originally consisted of more than 1 million entities and 5 million facts describing relationships between these entities. YAGO2 grounded entities, facts, and events in time and space, contained 446 million facts about 9.8 million entities, while YAGO3 added about 1 million more entities from non-English Wikipedia articles. YAGO3-10 a subset of YAGO3, containing entities which have a minimum of 10 relations each." }, { "dkey": "Paris-Lille-3D", "dval": "The Paris-Lille-3D is a Benchmark on Point Cloud Classification. The Point Cloud has been labeled entirely by hand with 50 different classes. The dataset consists of around 2km of Mobile Laser System point cloud acquired in two cities in France (Paris and Lille)." }, { "dkey": "CCAligned", "dval": "CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to multiple documents in different target language, it is possible to join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French)." }, { "dkey": "Opusparcus", "dval": "Opusparcus is a paraphrase corpus for six European languages: German, English, Finnish, French, Russian, and Swedish. The paraphrases are extracted from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV shows.\n\nFor each target language, the Opusparcus data have been partitioned into three types of data sets: training, development and test sets. The training sets are large, consisting of millions of sentence pairs, and have been compiled automatically, with the help of probabilistic ranking functions. The development and test sets consist of sentence pairs that have been annotated manually; each set contains approximately 1000 sentence pairs that have been verified to be acceptable paraphrases by two annotators." }, { "dkey": "Fashionpedia", "dval": "Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." } ]

[ "HotpotQA", "PAQ", "eQASC", "TweetQA", "OpenBookQA", "MultiReQA", "TVQA" ]

[ "MultiNLI", "SQuAD" ]

[ { "dkey": "MultiNLI", "dval": "The Multi-Genre Natural Language Inference (MultiNLI) dataset has 433K sentence pairs. Its size and mode of collection are modeled closely like SNLI. MultiNLI offers ten distinct genres (Face-to-face, Telephone, 9/11, Travel, Letters, Oxford University Press, Slate, Verbatim, Goverment and Fiction) of written and spoken English data. There are matched dev/test sets which are derived from the same sources as those in the training set, and mismatched sets which do not closely resemble any seen at training time." }, { "dkey": "SQuAD", "dval": "The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text. Because the questions and answers are produced by humans through crowdsourcing, it is more diverse than some other question-answering datasets. SQuAD 1.1 contains 107,785 question-answer pairs on 536 articles. SQuAD2.0 (open-domain SQuAD, SQuAD-Open), the latest version, combines the 100,000 questions in SQuAD1.1 with over 50,000 un-answerable questions written adversarially by crowdworkers in forms that are similar to the answerable ones." }, { "dkey": "HotpotQA", "dval": "HotpotQA is a question answering dataset collected on the English Wikipedia, containing about 113K crowd-sourced questions that are constructed to require the introduction paragraphs of two Wikipedia articles to answer. Each question in the dataset comes with the two gold paragraphs, as well as a list of sentences in these paragraphs that crowdworkers identify as supporting facts necessary to answer the question. \n\nA diverse range of reasoning strategies are featured in HotpotQA, including questions involving missing entities in the question, intersection questions (What satisfies property A and property B?), and comparison questions, where two entities are compared by a common attribute, among others. In the few-document distractor setting, the QA models are given ten paragraphs in which the gold paragraphs are guaranteed to be found; in the open-domain fullwiki setting, the models are only given the question and the entire Wikipedia. Models are evaluated on their answer accuracy and explainability, where the former is measured as overlap between the predicted and gold answers with exact match (EM) and unigram F1, and the latter concerns how well the predicted supporting fact sentences match human annotation (Supporting Fact EM/F1). A joint metric is also reported on this dataset, which encourages systems to perform well on both tasks simultaneously." }, { "dkey": "PAQ", "dval": "Probably Asked Questions (PAQ) is a very large resource of 65M automatically-generated QA-pairs. PAQ is a semi-structured Knowledge Base (KB) of 65M natural language QA-pairs, which models can memorise and/or learn to retrieve from. PAQ differs from traditional KBs in that questions and answers are stored in natural language, and that questions are generated such that they are likely to appear in ODQA datasets. PAQ is automatically constructed using a question generation model and Wikipedia." }, { "dkey": "eQASC", "dval": "This dataset contains 98k 2-hop explanations for questions in the QASC dataset, with annotations indicating if they are valid (~25k) or invalid (~73k) explanations.\n\nThis repository addresses the current lack of training data for distinguish valid multihop explanations from invalid, by providing three new datasets. The main one, eQASC, contains 98k explanation annotations for the multihop question answering dataset QASC, and is the first that annotates multiple candidate explanations for each answer.\n\nThe second dataset, eQASC-perturbed, is constructed by crowd-sourcing perturbations (while preserving their validity) of a subset of explanations in QASC, to test consistency and generalization of explanation prediction models. The third dataset eOBQA is constructed by adding explanation annotations to the OBQA dataset to test generalization of models trained on eQASC." }, { "dkey": "TweetQA", "dval": "With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effectiveness of many applications that rely on real-time knowledge. While previous question answering (QA) datasets have concentrated on formal text like news and Wikipedia, the first large-scale dataset for QA over social media data is presented. To make sure the tweets are meaningful and contain interesting information, tweets used by journalists to write news articles are gathered. Then human annotators are asked to write questions and answers upon these tweets. Unlike other QA datasets like SQuAD in which the answers are extractive, the answer are allowed to be abstractive. The task requires model to read a short tweet and a question and outputs a text phrase (does not need to be in the tweet) as the answer." }, { "dkey": "OpenBookQA", "dval": "OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject. It consists of 5,957 multiple-choice elementary-level science questions (4,957 train, 500 dev, 500 test), which probe the understanding of a small “book” of 1,326 core science facts and the application of these facts to novel situations. For training, the dataset includes a mapping from each question to the core science fact it was designed to probe. Answering OpenBookQA questions requires additional broad common knowledge, not contained in the book. The questions, by design, are answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm.\nAdditionally, the dataset includes a collection of 5,167 crowd-sourced common knowledge facts, and an expanded version of the train/dev/test questions where each question is associated with its originating core fact, a human accuracy score, a clarity score, and an anonymized crowd-worker ID." }, { "dkey": "MultiReQA", "dval": "MultiReQA is a cross-domain evaluation for retrieval question answering models. Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus. MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the MRQA shared task.\nMultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data." }, { "dkey": "TVQA", "dval": "The TVQA dataset is a large-scale vido dataset for video question answering. It is based on 6 popular TV shows (Friends, The Big Bang Theory, How I Met Your Mother, House M.D., Grey's Anatomy, Castle). It includes 152,545 QA pairs from 21,793 TV show clips. The QA pairs are split into the ratio of 8:1:1 for training, validation, and test sets. The TVQA dataset provides the sequence of video frames extracted at 3 FPS, the corresponding subtitles with the video clips, and the query consisting of a question and four answer candidates. Among the four answer candidates, there is only one correct answer." } ]

[ "TextVQA", "TDIUC", "KnowIT VQA", "MEDIQA-AnS", "AQUA", "COG" ]

[ "COCO", "CLEVR" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "CLEVR", "dval": "CLEVR (Compositional Language and Elementary Visual Reasoning) is a synthetic Visual Question Answering dataset. It contains images of 3D-rendered objects; each image comes with a number of highly compositional questions that fall into different categories. Those categories fall into 5 classes of tasks: Exist, Count, Compare Integer, Query Attribute and Compare Attribute. The CLEVR dataset consists of: a training set of 70k images and 700k questions, a validation set of 15k images and 150k questions, A test set of 15k images and 150k questions about objects, answers, scene graphs and functional programs for all train and validation images and questions. Each object present in the scene, aside of position, is characterized by a set of four attributes: 2 sizes: large, small, 3 shapes: square, cylinder, sphere, 2 material types: rubber, metal, 8 color types: gray, blue, brown, yellow, red, green, purple, cyan, resulting in 96 unique combinations." }, { "dkey": "TextVQA", "dval": "TextVQA is a dataset to benchmark visual reasoning based on text in images.\nTextVQA requires models to read and reason about text in images to answer questions about them. Specifically, models need to incorporate a new modality of text present in the images and reason over it to answer TextVQA questions.\n\nStatistics\n* 28,408 images from OpenImages\n* 45,336 questions\n* 453,360 ground truth answers" }, { "dkey": "TDIUC", "dval": "Task Directed Image Understanding Challenge (TDIUC) dataset is a Visual Question Answering dataset which consists of 1.6M questions and 170K images sourced from MS COCO and the Visual Genome Dataset. The image-question pairs are split into 12 categories and 4 additional evaluation matrices which help evaluate models’ robustness against answer imbalance and its ability to answer questions that require higher reasoning capability. The TDIUC dataset divides the VQA paradigm into 12 different task directed question types. These include questions that require a simpler task (e.g., object presence, color attribute) and more complex tasks (e.g., counting, positional reasoning). The dataset includes also an “Absurd” question category in which questions are irrelevant to the image contents to help balance the dataset." }, { "dkey": "KnowIT VQA", "dval": "KnowIT VQA is a video dataset with 24,282 human-generated question-answer pairs about The Big Bang Theory. The dataset combines visual, textual and temporal coherence reasoning together with knowledge-based questions, which need of the experience obtained from the viewing of the series to be answered." }, { "dkey": "MEDIQA-AnS", "dval": "The first summarization collection containing question-driven summaries of answers to consumer health questions. This dataset can be used to evaluate single or multi-document summaries generated by algorithms using extractive or abstractive approaches." }, { "dkey": "AQUA", "dval": "The question-answer (QA) pairs are automatically generated using state-of-the-art question generation methods based on paintings and comments provided in an existing art understanding dataset. The QA pairs are cleansed by crowdsourcing workers with respect to their grammatical correctness, answerability, and answers' correctness. The dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions." }, { "dkey": "COG", "dval": "A configurable visual question and answer dataset (COG) to parallel experiments in humans and animals. COG is much simpler than the general problem of video analysis, yet it addresses many of the problems relating to visual and logical reasoning and memory -- problems that remain challenging for modern deep learning architectures." } ]

[ "Interspeech 2021 Deep Noise Suppression Challenge", "CIHP", "DAVIS 2017", "THEODORE", "Airport" ]

[ "ImageNet", "COCO" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "Interspeech 2021 Deep Noise Suppression Challenge", "dval": "The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality.\n\nThis challenge has two two tracks:\n\nTrack 1: Real-Time Denoising track for wide band scenario\n\nThe noise suppressor must take less than the stride time Ts (in ms) to process a frame of size T (in ms) on an Intel Core i5 quad-core machine clocked at 2.4 GHz or equivalent processor. For example, Ts = T/2 for 50% overlap between frames. The total algorithmic latency allowed including the frame size T, stride time Ts, and any look ahead must be less than or equal to 40ms. For example, for a real-time system that receives 20ms audio chunks, if you use a frame length of 20ms with a stride of 10ms resulting in an algorithmic latency of 30ms, then you satisfy the latency requirements. If you use a frame of size 32ms with a stride of 16ms resulting in an algorithmic latency of 48ms, then your method does not satisfy the latency requirements as the total algorithmic latency exceeds 40ms. If your frame size plus stride T1=T+Ts is less than 40ms, then you can use up to (40-T1) ms future information.\n\nTrack 2: Real-Time Denoising track for full band scenario\n\nSatisfy Track 1 requirements but at 48 kHz." }, { "dkey": "CIHP", "dval": "The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images. Each image in CIHP is labeled with pixel-wise annotations on 20 categories and instance-level identification. The dataset can be used for the human part segmentation task." }, { "dkey": "DAVIS 2017", "dval": "DAVIS17 is a dataset for video object segmentation. It contains a total of 150 videos - 60 for training, 30 for validation, 60 for testing" }, { "dkey": "THEODORE", "dval": "Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset." }, { "dkey": "Airport", "dval": "The Airport dataset is a dataset for person re-identification which consists of 39,902 images and 9,651 identities across six cameras." } ]

[ "ConvAI2", "TableBank", "FRLL-Morphs", "BlendedMVS", "FERET-Morphs", "4DFAB" ]

[ "CASIA-WebFace", "MegaFace", "CelebA" ]

[ { "dkey": "CASIA-WebFace", "dval": "The CASIA-WebFace dataset is used for face verification and face identification tasks. The dataset contains 494,414 face images of 10,575 real identities collected from the web." }, { "dkey": "MegaFace", "dval": "MegaFace was a publicly available dataset which is used for evaluating the performance of face recognition algorithms with up to a million distractors (i.e., up to a million people who are not in the test set). MegaFace contains 1M images from 690K individuals with unconstrained pose, expression, lighting, and exposure. MegaFace captures many different subjects rather than many images of a small number of subjects. The gallery set of MegaFace is collected from a subset of Flickr. The probe set of MegaFace used in the challenge consists of two databases; Facescrub and FGNet. FGNet contains 975 images of 82 individuals, each with several images spanning ages from 0 to 69. Facescrub dataset contains more than 100K face images of 530 people. The MegaFace challenge evaluates performance of face recognition algorithms by increasing the numbers of “distractors” (going from 10 to 1M) in the gallery set. In order to evaluate the face recognition algorithms fairly, MegaFace challenge has two protocols including large or small training sets. If a training set has more than 0.5M images and 20K subjects, it is considered as large. Otherwise, it is considered as small.\n\nNOTE: This dataset has been retired." }, { "dkey": "CelebA", "dval": "CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "TableBank", "dval": "To address the need for a standard open domain table benchmark dataset, the author propose a novel weak supervision approach to automatically create the TableBank, which is orders of magnitude larger than existing human labeled datasets for table analysis. Distinct from traditional weakly supervised training set, our approach can obtain not only large scale but also high quality training data.\n\nNowadays, there are a great number of electronic documents on the web such as Microsoft Word (.docx) and Latex (.tex) files. These online documents contain mark-up tags for tables in their source code by nature. Intuitively, one can manipulate these source code by adding bounding box using the mark-up language within each document. For Word documents, the internal Office XML code can be modified where the borderline of each table is identified. For Latex documents, the tex code can be also modified where bounding boxes of tables are recognized. In this way, high-quality labeled data is created for a variety of domains such as business documents, official fillings, research papers etc, which is tremendously beneficial for large-scale table analysis tasks.\n\nThe TableBank dataset totally consists of 417,234 high quality labeled tables as well as their original documents in a variety of domains." }, { "dkey": "FRLL-Morphs", "dval": "FRLL-Morphs is a dataset of morphed faces based on images selected from the publicly available Face Research London Lab dataset [1].\n\nWe created the database by selecting similar looking pairs of people, and made 4 types of morphs for each pair using the following morphing tools: OpenCV [2], FaceMorpher [3], StyleGAN 2 [3], WebMorpher [4].\n\n\n[1] https://figshare.com/articles/dataset/Face_Research_Lab_London_Set/5047666\n[2] https://www.learnopencv.com/face-morph-using-opencv-cpp-python\n[3] https://github.com/yaopang/FaceMorpher/tree/master/facemorpher\n[4] https://github.com/NVlabs/stylegan2" }, { "dkey": "BlendedMVS", "dval": "BlendedMVS is a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. The dataset was created by applying a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes. Then, these mesh models were rendered to color images and depth maps." }, { "dkey": "FERET-Morphs", "dval": "FERET-Morphs is a dataset of morphed faces selected from the publicly available FERET dataset [1].\n\nWe created the database by selecting similar looking pairs of people, and made 3 types of morphs for each pair using the following morphing tools: OpenCV [2], FaceMorpher [3], StyleGAN 2 [3].\n\n\n[1] https://www.nist.gov/itl/products-and-services/color-feret-database\n[2] https://www.learnopencv.com/face-morph-using-opencv-cpp-python\n[3] https://github.com/yaopang/FaceMorpher/tree/master/facemorpher\n[4] https://github.com/NVlabs/stylegan2" }, { "dkey": "4DFAB", "dval": "4DFAB is a large scale database of dynamic high-resolution 3D faces which consists of recordings of 180 subjects captured in four different sessions spanning over a five-year period (2012 - 2017), resulting in a total of over 1,800,000 3D meshes. It contains 4D videos of subjects displaying both spontaneous and posed facial behaviours. The database can be used for both face and facial expression recognition, as well as behavioural biometrics. It can also be used to learn very powerful blendshapes for parametrising facial behaviour." } ]

[ "ParaBank", "MRPC", "PIT", "GLUE", "THEODORE", "PARADE" ]

[ "SNLI", "COCO" ]

[ { "dkey": "SNLI", "dval": "The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and asked to generate entailing, contradicting, and neutral sentences. Annotators were instructed to judge the relation between sentences given that they describe the same event. Each pair is labeled as “entailment”, “neutral”, “contradiction” or “-”, where “-” indicates that an agreement could not be reached." }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "ParaBank", "dval": "A large-scale English paraphrase dataset that surpasses prior work in both quantity and quality." }, { "dkey": "MRPC", "dval": "Microsoft Research Paraphrase Corpus (MRPC) is a corpus consists of 5,801 sentence pairs collected from newswire articles. Each pair is labelled if it is a paraphrase or not by human annotators. The whole set is divided into a training subset (4,076 sentence pairs of which 2,753 are paraphrases) and a test subset (1,725 pairs of which 1,147 are paraphrases)." }, { "dkey": "PIT", "dval": "Paraphrase and Semantic Similarity in Twitter (PIT) presents a constructed Twitter Paraphrase Corpus that contains 18,762 sentence pairs." }, { "dkey": "GLUE", "dval": "General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding tasks, including single-sentence tasks CoLA and SST-2, similarity and paraphrasing tasks MRPC, STS-B and QQP, and natural language inference tasks MNLI, QNLI, RTE and WNLI." }, { "dkey": "THEODORE", "dval": "Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset." }, { "dkey": "PARADE", "dval": "PARADE contains paraphrases that overlap very little at the lexical and syntactic level but are semantically equivalent based on computer science domain knowledge, as well as non-paraphrases that overlap greatly at the lexical and syntactic level but are not semantically equivalent based on this domain knowledge." } ]

[ "Paris-Lille-3D", "NDD20", "Toronto-3D", "ETH-XGaze", "LaPa", "OCID" ]

[ "COCO", "SBD" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "SBD", "dval": "The Semantic Boundaries Dataset (SBD) is a dataset for predicting pixels on the boundary of the object (as opposed to the inside of the object with semantic segmentation). The dataset consists of 11318 images from the trainval set of the PASCAL VOC2011 challenge, divided into 8498 training and 2820 test images. This dataset has object instance boundaries with accurate figure/ground masks that are also labeled with one of 20 Pascal VOC classes." }, { "dkey": "Paris-Lille-3D", "dval": "The Paris-Lille-3D is a Benchmark on Point Cloud Classification. The Point Cloud has been labeled entirely by hand with 50 different classes. The dataset consists of around 2km of Mobile Laser System point cloud acquired in two cities in France (Paris and Lille)." }, { "dkey": "NDD20", "dval": "Northumberland Dolphin Dataset 2020 (NDD20) is a challenging image dataset annotated for both coarse and fine-grained instance segmentation and categorisation. This dataset, the first release of the NDD, was created in response to the rapid expansion of computer vision into conservation research and the production of field-deployable systems suited to extreme environmental conditions -- an area with few open source datasets. NDD20 contains a large collection of above and below water images of two different dolphin species for traditional coarse and fine-grained segmentation." }, { "dkey": "Toronto-3D", "dval": "Toronto-3D is a large-scale urban outdoor point cloud dataset acquired by an MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of road and consists of about 78.3 million points. Point clouds has 10 attributes and classified in 8 labelled object classes." }, { "dkey": "ETH-XGaze", "dval": "Consists of over one million high-resolution images of varying gaze under extreme head poses. The dataset is collected from 110 participants with a custom hardware setup including 18 digital SLR cameras and adjustable illumination conditions, and a calibrated system to record ground truth gaze targets. \n\nOfficial competition on Codalab: https://competitions.codalab.org/competitions/28930" }, { "dkey": "LaPa", "dval": "A large-scale Landmark guided face Parsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks." }, { "dkey": "OCID", "dval": "Developing robot perception systems for handling objects in the real-world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating domain. This demands large quantities of ground truth data to rigorously evaluate the performance of algorithms.\n\nThe Object Cluttered Indoor Dataset is an RGBD-dataset containing point-wise labeled point-clouds for each object. The data was captured using two ASUS-PRO Xtion cameras that are positioned at different heights. It captures diverse settings of objects, background, context, sensor to scene distance, viewpoint angle and lighting conditions. The main purpose of OCID is to allow systematic comparison of existing object segmentation methods in scenes with increasing amount of clutter. In addition OCID does also provide ground-truth data for other vision tasks like object-classification and recognition." } ]

[ "THEODORE", "iSUN", "WN18RR", "BSDS500", "DPED", "E2E" ]

[ "DAVIS", "ImageNet", "COCO" ]

[ { "dkey": "DAVIS", "dval": "The Densely Annotation Video Segmentation dataset (DAVIS) is a high quality and high resolution densely annotated video segmentation dataset under two resolutions, 480p and 1080p. There are 50 video sequences with 3455 densely annotated frames in pixel level. 30 videos with 2079 frames are for training and 20 videos with 1376 frames are for validation." }, { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "THEODORE", "dval": "Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset." }, { "dkey": "iSUN", "dval": "iSUN is a ground truth of gaze traces on images from the SUN dataset. The collection is partitioned into 6,000 images for training, 926 for validation and 2,000 for test." }, { "dkey": "WN18RR", "dval": "WN18RR is a link prediction dataset created from WN18, which is a subset of WordNet. WN18 consists of 18 relations and 40,943 entities. However, many text triples are obtained by inverting triples from the training set. Thus the WN18RR dataset is created to ensure that the evaluation dataset does not have inverse relation test leakage. In summary, WN18RR dataset contains 93,003 triples with 40,943 entities and 11 relation types." }, { "dkey": "BSDS500", "dval": "Berkeley Segmentation Data Set 500 (BSDS500) is a standard benchmark for contour detection. This dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries. It includes 500 natural images with carefully annotated boundaries collected from multiple users. The dataset is divided into three parts: 200 for training, 100 for validation and the rest 200 for test." }, { "dkey": "DPED", "dval": "A large-scale dataset that consists of real photos captured from three different phones and one high-end reflex camera." }, { "dkey": "E2E", "dval": "End-to-End NLG Challenge (E2E) aims to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena." } ]

[ "VizWiz", "VQA-E", "OK-VQA", "KnowIT VQA", "ST-VQA", "TDIUC" ]

[ "COCO", "Visual7W" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "Visual7W", "dval": "Visual7W is a large-scale visual question answering (QA) dataset, with object-level groundings and multimodal answers. Each question starts with one of the seven Ws, what, where, when, who, why, how and which. It is collected from 47,300 COCO iamges and it has 327,929 QA pairs, together with 1,311,756 human-generated multiple-choices and 561,459 object groundings from 36,579 categories." }, { "dkey": "VizWiz", "dval": "The VizWiz-VQA dataset originates from a natural visual question answering setting where blind people each took an image and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. The proposed challenge addresses the following two tasks for this dataset: predict the answer to a visual question and (2) predict whether a visual question cannot be answered." }, { "dkey": "VQA-E", "dval": "VQA-E is a dataset for Visual Question Answering with Explanation, where the models are required to generate and explanation with the predicted answer. The VQA-E dataset is automatically derived from the VQA v2 dataset by synthesizing a textual explanation for each image-question-answer triple." }, { "dkey": "OK-VQA", "dval": "Outside Knowledge Visual Question Answering (OK-VQA) includes more than 14,000 questions that require external knowledge to answer." }, { "dkey": "KnowIT VQA", "dval": "KnowIT VQA is a video dataset with 24,282 human-generated question-answer pairs about The Big Bang Theory. The dataset combines visual, textual and temporal coherence reasoning together with knowledge-based questions, which need of the experience obtained from the viewing of the series to be answered." }, { "dkey": "ST-VQA", "dval": "ST-VQA aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the VQA process." }, { "dkey": "TDIUC", "dval": "Task Directed Image Understanding Challenge (TDIUC) dataset is a Visual Question Answering dataset which consists of 1.6M questions and 170K images sourced from MS COCO and the Visual Genome Dataset. The image-question pairs are split into 12 categories and 4 additional evaluation matrices which help evaluate models’ robustness against answer imbalance and its ability to answer questions that require higher reasoning capability. The TDIUC dataset divides the VQA paradigm into 12 different task directed question types. These include questions that require a simpler task (e.g., object presence, color attribute) and more complex tasks (e.g., counting, positional reasoning). The dataset includes also an “Absurd” question category in which questions are irrelevant to the image contents to help balance the dataset." } ]

[ "Icentia11K", "ConvAI2", "CC100", "VoxPopuli", "PTC", "SNIPS" ]

[ "MultiNLI", "SICK", "SentEval" ]

[ { "dkey": "MultiNLI", "dval": "The Multi-Genre Natural Language Inference (MultiNLI) dataset has 433K sentence pairs. Its size and mode of collection are modeled closely like SNLI. MultiNLI offers ten distinct genres (Face-to-face, Telephone, 9/11, Travel, Letters, Oxford University Press, Slate, Verbatim, Goverment and Fiction) of written and spoken English data. There are matched dev/test sets which are derived from the same sources as those in the training set, and mismatched sets which do not closely resemble any seen at training time." }, { "dkey": "SICK", "dval": "The Sentences Involving Compositional Knowledge (SICK) dataset is a dataset for compositional distributional semantics. It includes a large number of sentence pairs that are rich in the lexical, syntactic and semantic phenomena. Each pair of sentences is annotated in two dimensions: relatedness and entailment. The relatedness score ranges from 1 to 5, and Pearson’s r is used for evaluation; the entailment relation is categorical, consisting of entailment, contradiction, and neutral. There are 4439 pairs in the train split, 495 in the trial split used for development and 4906 in the test split. The sentence pairs are generated from image and video caption datasets before being paired up using some algorithm." }, { "dkey": "SentEval", "dval": "SentEval is a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. The set of tasks was selected based on what appears to be the community consensus regarding the appropriate evaluations for universal sentence representations. The toolkit comes with scripts to download and preprocess datasets, and an easy interface to evaluate sentence encoders." }, { "dkey": "Icentia11K", "dval": "Public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "CC100", "dval": "This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository." }, { "dkey": "VoxPopuli", "dval": "VoxPopuli is a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 16 languages and their aligned oral interpretations into 5 other languages totaling 5.1K hours." }, { "dkey": "PTC", "dval": "PTC is a collection of 344 chemical compounds represented as graphs which report the carcinogenicity for rats. There are 19 node labels for each node." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." } ]

[ "REDS", "CASIA-B", "Brain US", "ERA", "Kinetics-600" ]

[ "MPII", "3DPW" ]

[ { "dkey": "MPII", "dval": "The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 different human activities and the poses are manually annotated with up to 16 body joints." }, { "dkey": "3DPW", "dval": "The 3D Poses in the Wild dataset is the first dataset in the wild with accurate 3D poses for evaluation. While other datasets outdoors exist, they are all restricted to a small recording volume. 3DPW is the first one that includes video footage taken from a moving phone camera.\n\nThe dataset includes:\n\n\n60 video sequences.\n2D pose annotations.\n3D poses obtained with the method introduced in the paper.\nCamera poses for every frame in the sequences.\n3D body scans and 3D people models (re-poseable and re-shapeable). Each sequence contains its corresponding models.\n18 3D models in different clothing variations." }, { "dkey": "REDS", "dval": "The realistic and dynamic scenes (REDS) dataset was proposed in the NTIRE19 Challenge. The dataset is composed of 300 video sequences with resolution of 720×1,280, and each video has 100 frames, where the training set, the validation set and the testing set have 240, 30 and 30 videos, respectively" }, { "dkey": "CASIA-B", "dval": "CASIA-B is a large multiview gait database, which is created in January 2005. There are 124 subjects, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered. Besides the video files, we still provide human silhouettes extracted from video files. The detailed information about Dataset B and an evaluation framework can be found in this paper .\n\nThe format of the video filename in Dataset B is 'xxx-mm-nn-ttt.avi', where\n\nxxx: subject id, from 001 to 124.\nmm: walking status, can be 'nm' (normal), 'cl' (in a coat) or 'bg' (with a bag).\nnn: sequence number.\nttt: view angle, can be '000', '018', ..., '180'." }, { "dkey": "Brain US", "dval": "This brain anatomy segmentation dataset has 1300 2D US scans for training and 329 for testing. A total of 1629 in vivo B-mode US images were obtained from 20 different subjects (age<1 years old) who were treated between 2010 and 2016. The dataset contained subjects with IVH and without (healthy subjects but in risk of developing IVH). The US scans were collected using a Philips US machine with a C8-5 broadband curved array transducer using coronal and sagittal scan planes. For every collected image ventricles and septum pellecudi are manually segmented by an expert ultrasonographer. We split these images randomly into 1300 Training images and 329 Testing images for experiments. Note that these images are of size 512 × 512." }, { "dkey": "ERA", "dval": "Consists of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales." }, { "dkey": "Kinetics-600", "dval": "The Kinetics-600 is a large-scale action recognition dataset which consists of around 480K videos from 600 action categories. The 480K videos are divided into 390K, 30K, 60K for training, validation and test sets, respectively. Each video in the dataset is a 10-second clip of action moment annotated from raw YouTube video. It is an extensions of the Kinetics-400 dataset." } ]

[ "THEODORE", "Objects365", "G1020", "LogoDet-3K", "VT5000", "Open Images V4" ]

[ "ImageNet", "COCO" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "THEODORE", "dval": "Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset." }, { "dkey": "Objects365", "dval": "Objects365 is a large-scale object detection dataset, Objects365, which has 365 object categories over 600K training images. More than 10 million, high-quality bounding boxes are manually labeled through a three-step, carefully designed annotation pipeline. It is the largest object detection dataset (with full annotation) so far and establishes a more challenging benchmark for the community." }, { "dkey": "G1020", "dval": "A large publicly available retinal fundus image dataset for glaucoma classification called G1020. The dataset is curated by conforming to standard practices in routine ophthalmology and it is expected to serve as standard benchmark dataset for glaucoma detection. This database consists of 1020 high resolution colour fundus images and provides ground truth annotations for glaucoma diagnosis, optic disc and optic cup segmentation, vertical cup-to-disc ratio, size of neuroretinal rim in inferior, superior, nasal and temporal quadrants, and bounding box location for optic disc." }, { "dkey": "LogoDet-3K", "dval": "A logo detection dataset with full annotation, which has 3,000 logo categories, about 200,000 manually annotated logo objects and 158,652 images. LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets." }, { "dkey": "VT5000", "dval": "Includes 5000 spatially aligned RGBT image pairs with ground truth annotations. VT5000 has 11 challenges collected in different scenes and environments for exploring the robustness of algorithms." }, { "dkey": "Open Images V4", "dval": "Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, 15x more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images) are provided. The images often show complex scenes with several objects (8 annotated objects per image on average). Visual relationships between them are annotated, which support visual relationship detection, an emerging task that requires structured reasoning." } ]

[ "IMPPRES", "SNIPS", "Violin", "SherLIiC", "e-SNLI", "XNLI", "SNLI-VE" ]

[ "SNLI", "MultiNLI" ]

[ { "dkey": "SNLI", "dval": "The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and asked to generate entailing, contradicting, and neutral sentences. Annotators were instructed to judge the relation between sentences given that they describe the same event. Each pair is labeled as “entailment”, “neutral”, “contradiction” or “-”, where “-” indicates that an agreement could not be reached." }, { "dkey": "MultiNLI", "dval": "The Multi-Genre Natural Language Inference (MultiNLI) dataset has 433K sentence pairs. Its size and mode of collection are modeled closely like SNLI. MultiNLI offers ten distinct genres (Face-to-face, Telephone, 9/11, Travel, Letters, Oxford University Press, Slate, Verbatim, Goverment and Fiction) of written and spoken English data. There are matched dev/test sets which are derived from the same sources as those in the training set, and mismatched sets which do not closely resemble any seen at training time." }, { "dkey": "IMPPRES", "dval": "An IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "Violin", "dval": "Video-and-Language Inference is the task of joint multimodal understanding of video and text. Given a video clip with aligned subtitles as premise, paired with a natural language hypothesis based on the video content, a model needs to infer whether the hypothesis is entailed or contradicted by the given video clip. The Violin dataset is a dataset for this task which consists of 95,322 video-hypothesis pairs from 15,887 video clips, spanning over 582 hours of video. These video clips contain rich content with diverse temporal dynamics, event shifts, and people interactions, collected from two sources: (i) popular TV shows, and (ii) movie clips from YouTube channels." }, { "dkey": "SherLIiC", "dval": "SherLIiC is a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) ~960k unlabeled InfCands, and (ii) ~190k typed textual relations between Freebase entities extracted from the large entity-linked corpus ClueWeb09. Each InfCand consists of one of these relations, expressed as a lemmatized dependency path, and two argument placeholders, each linked to one or more Freebase types." }, { "dkey": "e-SNLI", "dval": "e-SNLI is used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets." }, { "dkey": "XNLI", "dval": "The Cross-lingual Natural Language Inference (XNLI) corpus is the extension of the Multi-Genre NLI (MultiNLI) corpus to 15 languages. The dataset was created by manually translating the validation and test sets of MultiNLI into each of those 15 languages. The English training set was machine translated for all languages. The dataset is composed of 122k train, 2490 validation and 5010 test examples." }, { "dkey": "SNLI-VE", "dval": "Visual Entailment (VE) consists of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal of a trained VE model is to predict whether the image semantically entails the text. SNLI-VE is a dataset for VE which is based on the Stanford Natural Language Inference corpus and Flickr30k dataset." } ]

[ "YCB-Video", "MuCo-3DHP", "SCUT-HEAD", "VIPeR" ]

[ "COCO", "MPII", "PoseTrack" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "MPII", "dval": "The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 different human activities and the poses are manually annotated with up to 16 body joints." }, { "dkey": "PoseTrack", "dval": "The PoseTrack dataset is a large-scale benchmark for multi-person pose estimation and tracking in videos. It requires not only pose estimation in single frames, but also temporal tracking across frames. It contains 514 videos including 66,374 frames in total, split into 300, 50 and 208 videos for training, validation and test set respectively. For training videos, 30 frames from the center are annotated. For validation and test videos, besides 30 frames from the center, every fourth frame is also annotated for evaluating long range articulated tracking. The annotations include 15 body keypoints location, a unique person id and a head bounding box for each person instance." }, { "dkey": "YCB-Video", "dval": "The YCB-Video dataset is a large-scale video dataset for 6D object pose estimation. provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames." }, { "dkey": "MuCo-3DHP", "dval": "MuCo-3DHP is a large scale training data set showing real images of sophisticated multi-person interactions and occlusions." }, { "dkey": "SCUT-HEAD", "dval": "Includes 4405 images with 111251 heads annotated." }, { "dkey": "VIPeR", "dval": "The Viewpoint Invariant Pedestrian Recognition (VIPeR) dataset includes 632 people and two outdoor cameras under different viewpoints and light conditions. Each person has one image per camera and each image has been scaled to be 128×48 pixels. It provides the pose angle of each person as 0° (front), 45°, 90° (right), 135°, and 180° (back)." } ]

[ "ConvAI2", "SNIPS", "FaceForensics", "I-HAZE", "DCASE 2014" ]

[ "ImageNet", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "FaceForensics", "dval": "FaceForensics is a video dataset consisting of more than 500,000 frames containing faces from 1004 videos that can be used to study image or video forgeries. All videos are downloaded from Youtube and are cut down to short continuous clips that contain mostly frontal faces. This dataset has two versions:\n\n\n\nSource-to-Target: where the authors reenact over 1000 videos with new facial expressions extracted from other videos, which e.g. can be used to train a classifier to detect fake images or videos.\n\n\n\nSelfreenactment: where the authors use Face2Face to reenact the facial expressions of videos with their own facial expressions as input to get pairs of videos, which e.g. can be used to train supervised generative refinement models." }, { "dkey": "I-HAZE", "dval": "The I-Haze dataset contains 25 indoor hazy images (size 2833×4657 pixels) training. It has 5 hazy images for validation along with their corresponding ground truth images." }, { "dkey": "DCASE 2014", "dval": "DCASE2014 is an audio classification benchmark." } ]

[ "FaceForensics", "ConvAI2", "AO-CLEVr", "WebNLG", "SUM" ]

[ "ImageNet", "CelebA" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CelebA", "dval": "CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age." }, { "dkey": "FaceForensics", "dval": "FaceForensics is a video dataset consisting of more than 500,000 frames containing faces from 1004 videos that can be used to study image or video forgeries. All videos are downloaded from Youtube and are cut down to short continuous clips that contain mostly frontal faces. This dataset has two versions:\n\n\n\nSource-to-Target: where the authors reenact over 1000 videos with new facial expressions extracted from other videos, which e.g. can be used to train a classifier to detect fake images or videos.\n\n\n\nSelfreenactment: where the authors use Face2Face to reenact the facial expressions of videos with their own facial expressions as input to get pairs of videos, which e.g. can be used to train supervised generative refinement models." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "AO-CLEVr", "dval": "AO-CLEVr is a new synthetic-images dataset containing images of \"easy\" Attribute-Object categories, based on the CLEVr. AO-CLEVr has attribute-object pairs created from 8 attributes: { red, purple, yellow, blue, green, cyan, gray, brown } and 3 object shapes {sphere, cube, cylinder}, yielding 24 attribute-object pairs. Each pair consists of 7500 images. Each image has a single object that consists of the attribute-object pair. The object is randomly assigned one of two sizes (small/large), one of two materials (rubber/metallic), a random position, and random lightning according to CLEVr defaults." }, { "dkey": "WebNLG", "dval": "The WebNLG corpus comprises of sets of triplets describing facts (entities and relations between them) and the corresponding facts in form of natural language text. The corpus contains sets with up to 7 triplets each along with one or more reference texts for each set. The test set is split into two parts: seen, containing inputs created for entities and relations belonging to DBpedia categories that were seen in the training data, and unseen, containing inputs extracted for entities and relations belonging to 5 unseen categories.\n\nInitially, the dataset was used for the WebNLG natural language generation challenge which consists of mapping the sets of triplets to text, including referring expression generation, aggregation, lexicalization, surface realization, and sentence segmentation.\nThe corpus is also used for a reverse task of triplets extraction.\n\nVersioning history of the dataset can be found here.\n\nIt's also available here: https://huggingface.co/datasets/web_nlg\nNote: \"The v3 release (release_v3.0_en, release_v3.0_ru) for the WebNLG2020 challenge also supports a semantic parsing task.\"" }, { "dkey": "SUM", "dval": "SUM is a new benchmark dataset of semantic urban meshes which covers about 4 km2 in Helsinki (Finland), with six classes: Ground, Vegetation, Building, Water, Vehicle, and Boat.\n\nThe authors used Helsinki 3D textured meshes as input and annotated them as a benchmark dataset of semantic urban meshes. The Helsinki's raw dataset covers about 12 km2 and was generated in 2017 from oblique aerial images that have about a 7.5 cm ground sampling distance (GSD) using an off-the-shelf commercial software namely ContextCapture.\n\nThe entire region of Helsinki is split into tiles, and each of them covers about 250 m2." } ]

[ "ExPose", "Twitter100k", "Raindrop", "M2E2", "JAAD", "THEODORE" ]

[ "MARS", "CUHK03" ]

[ { "dkey": "MARS", "dval": "MARS (Motion Analysis and Re-identification Set) is a large scale video based person reidentification dataset, an extension of the Market-1501 dataset. It has been collected from six near-synchronized cameras. It consists of 1,261 different pedestrians, who are captured by at least 2 cameras. The variations in poses, colors and illuminations of pedestrians, as well as the poor image quality, make it very difficult to yield high matching accuracy. Moreover, the dataset contains 3,248 distractors in order to make it more realistic. Deformable Part Model and GMMCP tracker were used to automatically generate the tracklets (mostly 25-50 frames long)." }, { "dkey": "CUHK03", "dval": "The CUHK03 consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector. The dataset also provides 20 random train/test splits in which 100 identities are selected for testing and the rest for training" }, { "dkey": "ExPose", "dval": "Curates a dataset of SMPL-X fits on in-the-wild images." }, { "dkey": "Twitter100k", "dval": "Twitter100k is a large-scale dataset for weakly supervised cross-media retrieval." }, { "dkey": "Raindrop", "dval": "Raindrop is a set of image pairs, where\neach pair contains exactly the same background scene, yet\none is degraded by raindrops and the other one is free from\nraindrops. To obtain this, the images are captured through two pieces of exactly the\nsame glass: one sprayed with water, and the other is left\nclean. The dataset consists of 1,119 pairs of images, with various\nbackground scenes and raindrops. They were captured with a Sony A6000\nand a Canon EOS 60." }, { "dkey": "M2E2", "dval": "Aims to extract events and their arguments from multimedia documents. Develops the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments." }, { "dkey": "JAAD", "dval": "JAAD is a dataset for studying joint attention in the context of autonomous driving. The focus is on pedestrian and driver behaviors at the point of crossing and factors that influence them. To this end, JAAD dataset provides a richly annotated collection of 346 short video clips (5-10 sec long) extracted from over 240 hours of driving footage. These videos filmed in several locations in North America and Eastern Europe represent scenes typical for everyday urban driving in various weather conditions.\n\nBounding boxes with occlusion tags are provided for all pedestrians making this dataset suitable for pedestrian detection.\n\nBehavior annotations specify behaviors for pedestrians that interact with or require attention of the driver. For each video there are several tags (weather, locations, etc.) and timestamped behavior labels from a fixed list (e.g. stopped, walking, looking, etc.). In addition, a list of demographic attributes is provided for each pedestrian (e.g. age, gender, direction of motion, etc.) as well as a list of visible traffic scene elements (e.g. stop sign, traffic signal, etc.) for each frame.\n\nPaper: Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior" }, { "dkey": "THEODORE", "dval": "Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset." } ]

[ "SNIPS", "HVU", "SemanticKITTI", "7-Scenes", "Virtual KITTI" ]

[ "KITTI", "Cityscapes" ]

[ { "dkey": "KITTI", "dval": "KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist." }, { "dkey": "Cityscapes", "dval": "Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. Data was captured in 50 cities during several months, daytimes, and good weather conditions. It was originally recorded as video so the frames were manually selected to have the following features: large number of dynamic objects, varying scene layout, and varying background." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "HVU", "dval": "HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene. HVU contains approx.~572k videos in total with 9 million annotations for training, validation, and test set spanning over 3142 labels. HVU encompasses semantic aspects defined on categories of scenes, objects, actions, events, attributes, and concepts which naturally captures the real-world scenarios." }, { "dkey": "SemanticKITTI", "dval": "SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. The dataset consists of 22 sequences. Overall, the dataset provides 23201 point clouds for training and 20351 for testing." }, { "dkey": "7-Scenes", "dval": "The 7-Scenes dataset is a collection of tracked RGB-D camera frames. The dataset may be used for evaluation of methods for different applications such as dense tracking and mapping and relocalization techniques.\nAll scenes were recorded from a handheld Kinect RGB-D camera at 640×480 resolution. The dataset creators use an implementation of the KinectFusion system to obtain the ‘ground truth’ camera tracks, and a dense 3D model. Several sequences were recorded per scene by different users, and split into distinct training and testing sequence sets." }, { "dkey": "Virtual KITTI", "dval": "Virtual KITTI is a photo-realistic synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation, optical flow, and depth estimation.\n\nVirtual KITTI contains 50 high-resolution monocular videos (21,260 frames) generated from five different virtual worlds in urban settings under different imaging and weather conditions. These worlds were created using the Unity game engine and a novel real-to-virtual cloning method. These photo-realistic synthetic videos are automatically, exactly, and fully annotated for 2D and 3D multi-object tracking and at the pixel level with category, instance, flow, and depth labels (cf. below for download links)." } ]

[ "Melinda", "SNIPS", "ConvAI2", "ACDC", "DCASE 2014" ]

[ "ImageNet", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "Melinda", "dval": "Introduces a new dataset, MELINDA, for Multimodal biomEdicaL experImeNt methoD clAssification. The dataset is collected in a fully automated distant supervision manner, where the labels are obtained from an existing curated database, and the actual contents are extracted from papers associated with each of the records in the database." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "ACDC", "dval": "The goal of the Automated Cardiac Diagnosis Challenge (ACDC) challenge is to:\n\n\ncompare the performance of automatic methods on the segmentation of the left ventricular endocardium and epicardium as the right ventricular endocardium for both end diastolic and end systolic phase instances;\ncompare the performance of automatic methods for the classification of the examinations in five classes (normal case, heart failure with infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, abnormal right ventricle).\n\nThe overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in particular diastolic volume and ejection fraction). The dataset is composed of 150 exams (all from different patients) divided into 5 evenly distributed subgroups (4 pathological plus 1 healthy subject groups) as described below. Furthermore, each patient comes with the following additional information : weight, height, as well as the diastolic and systolic phase instants.\n\nThe database is made available to participants through two datasets from the dedicated online evaluation website after a personal registration: i) a training dataset of 100 patients along with the corresponding manual references based on the analysis of one clinical expert; ii) a testing dataset composed of 50 new patients, without manual annotations but with the patient information given above. The raw input images are provided through the Nifti format." }, { "dkey": "DCASE 2014", "dval": "DCASE2014 is an audio classification benchmark." } ]

[ "THEODORE", "E2E", "MVTecAD", "SuperGLUE", "MIMIC-CXR" ]

[ "Caltech-101", "CIFAR-10" ]

[ { "dkey": "Caltech-101", "dval": "The Caltech101 dataset contains images from 101 object categories (e.g., “helicopter”, “elephant” and “chair” etc.) and a background category that contains the images not from the 101 object categories. For each object category, there are about 40 to 800 images, while most classes have about 50 images. The resolution of the image is roughly about 300×200 pixels." }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "THEODORE", "dval": "Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset." }, { "dkey": "E2E", "dval": "End-to-End NLG Challenge (E2E) aims to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena." }, { "dkey": "MVTecAD", "dval": "MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.\n\nThere are two common metrics: Detection AUROC and Segmentation (or pixelwise) AUROC\n\nDetection (or, classification) methods output single float (anomaly score) per input test image. \n\nSegmentation methods output anomaly probability for each pixel. \n\"To assess segmentation performance, we evaluate the relative per-region overlap of the segmentation with the ground truth. To get an additional performance measure that is independent of the determined threshold, we compute the area under the receiver operating characteristic curve (ROC AUC). We define the true positive rate as the percentage of pixels that were correctly classified as anomalous\" [1]\nLater segmentation metric was improved to balance regions with small and large area, see PRO-AUC and other in [2]\n\n[1] Paul Bergmann et al, \"MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection\"\n[2] Bergmann, P., Batzner, K., Fauser, M. et al. The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. Int J Comput Vis (2021). https://doi.org/10.1007/s11263-020-01400-4" }, { "dkey": "SuperGLUE", "dval": "SuperGLUE is a benchmark dataset designed to pose a more rigorous test of language understanding than GLUE. SuperGLUE has the same high-level motivation as GLUE: to provide a simple, hard-to-game measure of progress toward general-purpose language understanding technologies for English. SuperGLUE follows the basic design of GLUE: It consists of a public leaderboard built around eight language understanding tasks, drawing on existing data, accompanied by a single-number\nperformance metric, and an analysis toolkit. However, it improves upon GLUE in several ways:\n\n\nMore challenging tasks: SuperGLUE retains the two hardest tasks in GLUE. The remaining tasks were identified from those submitted to an open call for task proposals and were selected based on difficulty for current NLP approaches.\nMore diverse task formats: The task formats in GLUE are limited to sentence- and sentence-pair classification. The authors expand the set of task formats in SuperGLUE to include\ncoreference resolution and question answering (QA).\nComprehensive human baselines: the authors include human performance estimates for all benchmark tasks, which verify that substantial headroom exists between a strong BERT-based baseline and human performance.\nImproved code support: SuperGLUE is distributed with a new, modular toolkit for work on pretraining, multi-task learning, and transfer learning in NLP, built around standard tools including PyTorch (Paszke et al., 2017) and AllenNLP (Gardner et al., 2017).\nRefined usage rules: The conditions for inclusion on the SuperGLUE leaderboard were revamped to ensure fair competition, an informative leaderboard, and full credit\nassignment to data and task creators." }, { "dkey": "MIMIC-CXR", "dval": "MIMIC-CXR from Massachusetts Institute of Technology presents 371,920 chest X-rays associated with 227,943 imaging studies from 65,079 patients. The studies were performed at Beth Israel Deaconess Medical Center in Boston, MA." } ]

[ "SNIPS", "SuperGLUE", "FFHQ", "DailyDialog++" ]

[ "ImageNet", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "SNIPS", "dval": "The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:\n\n\nSearchCreativeWork (e.g. Find me the I, Robot television show),\nGetWeather (e.g. Is it windy in Boston, MA right now?),\nBookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),\nPlayMusic (e.g. Play the last track from Beyoncé off Spotify),\nAddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),\nRateBook (e.g. Give 6 stars to Of Mice and Men),\nSearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).\nThe training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent." }, { "dkey": "SuperGLUE", "dval": "SuperGLUE is a benchmark dataset designed to pose a more rigorous test of language understanding than GLUE. SuperGLUE has the same high-level motivation as GLUE: to provide a simple, hard-to-game measure of progress toward general-purpose language understanding technologies for English. SuperGLUE follows the basic design of GLUE: It consists of a public leaderboard built around eight language understanding tasks, drawing on existing data, accompanied by a single-number\nperformance metric, and an analysis toolkit. However, it improves upon GLUE in several ways:\n\n\nMore challenging tasks: SuperGLUE retains the two hardest tasks in GLUE. The remaining tasks were identified from those submitted to an open call for task proposals and were selected based on difficulty for current NLP approaches.\nMore diverse task formats: The task formats in GLUE are limited to sentence- and sentence-pair classification. The authors expand the set of task formats in SuperGLUE to include\ncoreference resolution and question answering (QA).\nComprehensive human baselines: the authors include human performance estimates for all benchmark tasks, which verify that substantial headroom exists between a strong BERT-based baseline and human performance.\nImproved code support: SuperGLUE is distributed with a new, modular toolkit for work on pretraining, multi-task learning, and transfer learning in NLP, built around standard tools including PyTorch (Paszke et al., 2017) and AllenNLP (Gardner et al., 2017).\nRefined usage rules: The conditions for inclusion on the SuperGLUE leaderboard were revamped to ensure fair competition, an informative leaderboard, and full credit\nassignment to data and task creators." }, { "dkey": "FFHQ", "dval": "Flickr-Faces-HQ (FFHQ) consists of 70,000 high-quality PNG images at 1024×1024 resolution and contains considerable variation in terms of age, ethnicity and image background. It also has good coverage of accessories such as eyeglasses, sunglasses, hats, etc. The images were crawled from Flickr, thus inheriting all the biases of that website, and automatically aligned and cropped using dlib. Only images under permissive licenses were collected. Various automatic filters were used to prune the set, and finally Amazon Mechanical Turk was used to remove the occasional statues, paintings, or photos of photos." }, { "dkey": "DailyDialog++", "dval": "Consists of (i) five relevant responses for each context and (ii) five adversarially crafted irrelevant responses for each context." } ]

[ "LDC2020T02", "WiC", "AW-OIE", "QAMR", "FrameNet" ]

[ "GLUE", "SQuAD" ]

[ { "dkey": "GLUE", "dval": "General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding tasks, including single-sentence tasks CoLA and SST-2, similarity and paraphrasing tasks MRPC, STS-B and QQP, and natural language inference tasks MNLI, QNLI, RTE and WNLI." }, { "dkey": "SQuAD", "dval": "The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text. Because the questions and answers are produced by humans through crowdsourcing, it is more diverse than some other question-answering datasets. SQuAD 1.1 contains 107,785 question-answer pairs on 536 articles. SQuAD2.0 (open-domain SQuAD, SQuAD-Open), the latest version, combines the 100,000 questions in SQuAD1.1 with over 50,000 un-answerable questions written adversarially by crowdworkers in forms that are similar to the answerable ones." }, { "dkey": "LDC2020T02", "dval": "Abstract Meaning Representation (AMR) Annotation Release 3.0 was developed by the Linguistic Data Consortium (LDC), SDL/Language Weaver, Inc., the University of Colorado's Computational Language and Educational Research group and the Information Sciences Institute at the University of Southern California. It contains a sembank (semantic treebank) of over 59,255 English natural language sentences from broadcast conversations, newswire, weblogs, web discussion forums, fiction and web text. This release adds new data to, and updates material contained in, Abstract Meaning Representation 2.0 (LDC2017T10), specifically: more annotations on new and prior data, new or improved PropBank-style frames, enhanced quality control, and multi-sentence annotations.\n\nAMR captures \"who is doing what to whom\" in a sentence. Each sentence is paired with a graph that represents its whole-sentence meaning in a tree-structure. AMR utilizes PropBank frames, non-core semantic roles, within-sentence coreference, named entity annotation, modality, negation, questions, quantities, and so on to represent the semantic structure of a sentence largely independent of its syntax." }, { "dkey": "WiC", "dval": "WiC is a benchmark for the evaluation of context-sensitive word embeddings. WiC is framed as a binary classification task. Each instance in WiC has a target word w, either a verb or a noun, for which two contexts are provided. Each of these contexts triggers a specific meaning of w. The task is to identify if the occurrences of w in the two contexts correspond to the same meaning or not. In fact, the dataset can also be viewed as an application of Word Sense Disambiguation in practise." }, { "dkey": "AW-OIE", "dval": "All Words Open IE (AW-OIE) is an open information extraction dataset derived from Question-Answer Meaning Representation (QAMR) dataset." }, { "dkey": "QAMR", "dval": "Question-Answer Meaning Representation (QAMR) represents a predicate-argument structure of a sentence with a set of question-answer pairs, so that annotations can be easily provided by non-experts. QAMR is a dataset of over 5,000 sentences and 100,000 questions created by crowdsourcing workers." }, { "dkey": "FrameNet", "dval": "FrameNet is a linguistic knowledge graph containing information about lexical and predicate argument semantics of the English language. FrameNet contains two distinct entity classes: frames and lexical units, where a frame is a meaning and a lexical unit is a single meaning for a word." } ]

[ "TVQA", "COCO-Tasks", "dMelodies", "ConvAI2", "PHM2017", "CommonsenseQA" ]

[ "ImageNet", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "TVQA", "dval": "The TVQA dataset is a large-scale vido dataset for video question answering. It is based on 6 popular TV shows (Friends, The Big Bang Theory, How I Met Your Mother, House M.D., Grey's Anatomy, Castle). It includes 152,545 QA pairs from 21,793 TV show clips. The QA pairs are split into the ratio of 8:1:1 for training, validation, and test sets. The TVQA dataset provides the sequence of video frames extracted at 3 FPS, the corresponding subtitles with the video clips, and the query consisting of a question and four answer candidates. Among the four answer candidates, there is only one correct answer." }, { "dkey": "COCO-Tasks", "dval": "Comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated." }, { "dkey": "dMelodies", "dval": "dMelodies is dataset of simple 2-bar melodies generated using 9 independent latent factors of variation where each data point represents a unique melody based on the following constraints:\n- Each melody will correspond to a unique scale (major, minor, blues, etc.).\n- Each melody plays the arpeggios using the standard I-IV-V-I cadence chord pattern.\n- Bar 1 plays the first 2 chords (6 notes), Bar 2 plays the second 2 chords (6 notes).\n- Each played note is an 8th note." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "PHM2017", "dval": "PHM2017 is a new dataset consisting of 7,192 English tweets across six diseases and conditions: Alzheimer’s Disease, heart attack (any severity), Parkinson’s disease, cancer (any type), Depression (any severity), and Stroke. The Twitter search API was used to retrieve the data using the colloquial disease names as search keywords, with the expectation of retrieving a high-recall, low precision dataset. After removing the re-tweets and replies, the tweets were manually annotated. The labels are:\n\n\nself-mention. The tweet contains a health mention with a health self-report of the Twitter account owner, e.g., \"However, I worked hard and ran for Tokyo Mayer Election Campaign in January through February, 2014, without publicizing the cancer.\"\nother-mention. The tweet contains a health mention of a health report about someone other than the account owner, e.g., \"Designer with Parkinson’s couldn’t work then engineer invents bracelet + changes her world\"\nawareness. The tweet contains the disease name, but does not mention a specific person, e.g., \"A Month Before a Heart Attack, Your Body Will Warn You With These 8 Signals\"\nnon-health. The tweet contains the disease name, but the tweet topic is not about health. \"Now I can have cancer on my wall for all to see <3\"" }, { "dkey": "CommonsenseQA", "dval": "The CommonsenseQA is a dataset for commonsense question answering task. The dataset consists of 12,247 questions with 5 choices each.\nThe dataset was generated by Amazon Mechanical Turk workers in the following process (an example is provided in parentheses):\n\n\na crowd worker observes a source concept from ConceptNet (“River”) and three target concepts (“Waterfall”, “Bridge”, “Valley”) that are all related by the same ConceptNet relation (“AtLocation”),\nthe worker authors three questions, one per target concept, such that only that particular target concept is the answer, while the other two distractor concepts are not, (“Where on a river can you hold a cup upright to catch water on a sunny day?”, “Where can I stand on a river to see water falling without getting wet?”, “I’m crossing the river, my feet are wet but my body is dry, where am I?”)\nfor each question, another worker chooses one additional distractor from Concept Net (“pebble”, “stream”, “bank”), and the author another distractor (“mountain”, “bottom”, “island”) manually." } ]

[ "CommonsenseQA", "ConvAI2", "LUNA", "Syn2Real" ]

[ "OTB", "VOT2016" ]

[ { "dkey": "OTB", "dval": "Object Tracking Benchmark (OTB) is a visual tracking benchmark that is widely used to evaluate the performance of a visual tracking algorithm. The dataset contains a total of 100 sequences and each is annotated frame-by-frame with bounding boxes and 11 challenge attributes. OTB-2013 dataset contains 51 sequences and the OTB-2015 dataset contains all 100 sequences of the OTB dataset." }, { "dkey": "VOT2016", "dval": "VOT2016 is a video dataset for visual object tracking. It contains 60 video clips and 21,646 corresponding ground truth maps with pixel-wise annotation of salient objects." }, { "dkey": "CommonsenseQA", "dval": "The CommonsenseQA is a dataset for commonsense question answering task. The dataset consists of 12,247 questions with 5 choices each.\nThe dataset was generated by Amazon Mechanical Turk workers in the following process (an example is provided in parentheses):\n\n\na crowd worker observes a source concept from ConceptNet (“River”) and three target concepts (“Waterfall”, “Bridge”, “Valley”) that are all related by the same ConceptNet relation (“AtLocation”),\nthe worker authors three questions, one per target concept, such that only that particular target concept is the answer, while the other two distractor concepts are not, (“Where on a river can you hold a cup upright to catch water on a sunny day?”, “Where can I stand on a river to see water falling without getting wet?”, “I’m crossing the river, my feet are wet but my body is dry, where am I?”)\nfor each question, another worker chooses one additional distractor from Concept Net (“pebble”, “stream”, “bank”), and the author another distractor (“mountain”, “bottom”, “island”) manually." }, { "dkey": "ConvAI2", "dval": "The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.\n\nTo avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”\n\nThe training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively." }, { "dkey": "LUNA", "dval": "The LUNA challenges provide datasets for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified." }, { "dkey": "Syn2Real", "dval": "Syn2Real, a synthetic-to-real visual domain adaptation benchmark meant to encourage further development of robust domain transfer methods. The goal is to train a model on a synthetic \"source\" domain and then update it so that its performance improves on a real \"target\" domain, without using any target annotations. It includes three tasks, illustrated in figures above: the more traditional closed-set classification task with a known set of categories; the less studied open-set classification task with unknown object categories in the target domain; and the object detection task, which involves localizing instances of objects by predicting their bounding boxes and corresponding class labels." } ]

[ "TechQA", "IMPPRES", "BraTS 2017", "CTPelvic1K", "UCLA Aerial Event Dataset", "CC-19", "e-SNLI" ]

[ "ImageNet", "Penn Treebank", "CIFAR-10" ]

[ { "dkey": "ImageNet", "dval": "The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.\nThe publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld.\nILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”.\nThe ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.\n\n\nTotal number of non-empty WordNet synsets: 21841\nTotal number of images: 14197122\nNumber of images with bounding box annotations: 1,034,908\nNumber of synsets with SIFT features: 1000\nNumber of images with SIFT features: 1.2 million" }, { "dkey": "Penn Treebank", "dval": "The English Penn Treebank (PTB) corpus, and in particular the section of the corpus corresponding to the articles of Wall Street Journal (WSJ), is one of the most known and used corpus for the evaluation of models for sequence labelling. The task consists of annotating each word with its Part-of-Speech tag. In the most common split of this corpus, sections from 0 to 18 are used for training (38 219 sentences, 912 344 tokens), sections from 19 to 21 are used for validation (5 527 sentences, 131 768 tokens), and sections from 22 to 24 are used for testing (5 462 sentences, 129 654 tokens).\nThe corpus is also commonly used for character-level and word-level Language Modelling." }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "TechQA", "dval": "TECHQA is a domain-adaptation question answering dataset for the technical support domain. The TECHQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size – 600 training, 310 dev, and 490 evaluation question/answer pairs – thus reflecting the cost of creating large labeled datasets with actual data. Consequently, TECHQA is meant to stimulate research in domain adaptation rather than being a resource to build QA systems from scratch. The dataset was obtained by crawling the IBM Developer and IBM DeveloperWorks forums for questions with accepted answers that appear in a published IBM Technote—a technical document that addresses a specific technical issue." }, { "dkey": "IMPPRES", "dval": "An IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types." }, { "dkey": "BraTS 2017", "dval": "The BRATS2017 dataset. It contains 285 brain tumor MRI scans, with four MRI modalities as T1, T1ce, T2, and Flair for each scan. The dataset also provides full masks for brain tumors, with labels for ED, ET, NET/NCR. The segmentation evaluation is based on three tasks: WT, TC and ET segmentation." }, { "dkey": "CTPelvic1K", "dval": "Curates a large pelvic CT dataset pooled from multiple sources and different manufacturers, including 1, 184 CT volumes and over 320, 000 slices with different resolutions and a variety of the above-mentioned appearance variations." }, { "dkey": "UCLA Aerial Event Dataset", "dval": "The UCLA Aerial Event Dataest has been captured by a low-cost hex-rotor with a GoPro camera, which is able to eliminate the high frequency vibration of the camera and hold in air autonomously through a GPS and a barometer. It can also fly 20 ∼ 90m above the ground and stays 5 minutes in air. \n\nThis hex-rotor has been used to take the set of videos in the dataset, captured in different places: hiking routes, parking lots, camping sites, picnic areas with shelters, restrooms, tables, trash bins and BBQ ovens. By detecting/tracking humans and objects in the videos, the videos can be annotated with events.\n\nThe original videos are pre-processed, including camera calibration and frame registration. After pre-processing, there are totally 27 videos in the dataset, the length of which ranges from 2 minutes to 5 minutes. Each video has annotations with hierarchical semantic information of objects, roles, events and groups in the videos." }, { "dkey": "CC-19", "dval": "CC-19 is a small new dataset related to the latest family of coronavirus i.e. COVID-19. The proposed dataset “CC-19” contains 34,006 CT scan slices (images) belonging to 98 subjects out of which 28,395 CT scan slices belong to positive COVID patients." }, { "dkey": "e-SNLI", "dval": "e-SNLI is used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets." } ]

[ "SAVEE", "SpeakingFaces", "ADVANCE", "VGG-Sound", "AVSD" ]

[ "AVA", "LRW" ]

[ { "dkey": "AVA", "dval": "AVA is a project that provides audiovisual annotations of video for improving our understanding of human activity. Each of the video clips has been exhaustively annotated by human annotators, and together they represent a rich variety of scenes, recording conditions, and expressions of human activity. There are annotations for:\n\n\nKinetics (AVA-Kinetics) - a crossover between AVA and Kinetics. In order to provide localized action labels on a wider variety of visual scenes, authors provide AVA action labels on videos from Kinetics-700, nearly doubling the number of total annotations, and increasing the number of unique videos by over 500x. \nActions (AvA Actions) - the AVA dataset densely annotates 80 atomic visual actions in 430 15-minute movie clips, where actions are localized in space and time, resulting in 1.62M action labels with multiple labels per human occurring frequently. \nSpoken Activity (AVA ActiveSpeaker, AVA Speech). AVA ActiveSpeaker: associates speaking activity with a visible face, on the AVA v1.0 videos, resulting in 3.65 million frames labeled across ~39K face tracks. AVA Speech densely annotates audio-based speech activity in AVA v1.0 videos, and explicitly labels 3 background noise conditions, resulting in ~46K labeled segments spanning 45 hours of data." }, { "dkey": "LRW", "dval": "The Lip Reading in the Wild (LRW) dataset a large-scale audio-visual database that contains 500 different words from over 1,000 speakers. Each utterance has 29 frames, whose boundary is centered around the target word. The database is divided into training, validation and test sets. The training set contains at least 800 utterances for each class while the validation and test sets contain 50 utterances." }, { "dkey": "SAVEE", "dval": "The Surrey Audio-Visual Expressed Emotion (SAVEE) dataset was recorded as a pre-requisite for the development of an automatic emotion recognition system. The database consists of recordings from 4 male actors in 7 different emotions, 480 British English utterances in total. The sentences were chosen from the standard TIMIT corpus and phonetically-balanced for each emotion. The data were recorded in a visual media lab with high quality audio-visual equipment, processed and labeled. To check the quality of performance, the recordings were evaluated by 10 subjects under audio, visual and audio-visual conditions. Classification systems were built using standard features and classifiers for each of the audio, visual and audio-visual modalities, and speaker-independent recognition rates of 61%, 65% and 84% achieved respectively." }, { "dkey": "SpeakingFaces", "dval": "SpeakingFaces is a publicly-available large-scale dataset developed to support multimodal machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human-computer interaction (HCI), biometric authentication, recognition systems, domain transfer, and speech recognition. SpeakingFaces is comprised of well-aligned high-resolution thermal and visual spectra image streams of fully-framed faces synchronized with audio recordings of each subject speaking approximately 100 imperative phrases." }, { "dkey": "ADVANCE", "dval": "The AuDio Visual Aerial sceNe reCognition datasEt (ADVANCE) is a brand-new multimodal learning dataset, which aims to explore the contribution of both audio and conventional visual messages to scene recognition. This dataset in summary contains 5075 pairs of geotagged aerial images and sounds, classified into 13 scene classes, i.e., airport, sports land, beach, bridge, farmland, forest, grassland, harbor, lake, orchard, residential area, shrub land, and train station." }, { "dkey": "VGG-Sound", "dval": "Consists of more than 210k videos for 310 audio classes." }, { "dkey": "AVSD", "dval": "The Audio Visual Scene-Aware Dialog (AVSD) dataset, or DSTC7 Track 3, is a audio-visual dataset for dialogue understanding. The goal with the dataset and track was to design systems to generate responses in a dialog about a video, given the dialog history and audio-visual content of the video." } ]

[ "BraTS 2017", "NAS-Bench-101", "Places", "GQA", "KLEJ", "COCO-Text" ]

[ "COCO", "CIFAR-10" ]

[ { "dkey": "COCO", "dval": "The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.\n\nSplits:\nThe first version of MS COCO dataset was released in 2014. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was released, including all the previous test images and 40K new images.\n\nBased on community feedback, in 2017 the training/validation split was changed from 83K/41K to 118K/5K. The new split uses the same images and annotations. The 2017 test set is a subset of 41K images of the 2015 test set. Additionally, the 2017 release contains a new unannotated dataset of 123K images.\n\nAnnotations:\nThe dataset has annotations for\n\n\nobject detection: bounding boxes and per-instance segmentation masks with 80 object categories,\ncaptioning: natural language descriptions of the images (see MS COCO Captions),\nkeypoints detection: containing more than 200,000 images and 250,000 person instances labeled with keypoints (17 possible keypoints, such as left eye, nose, right hip, right ankle),\nstuff image segmentation – per-pixel segmentation masks with 91 stuff categories, such as grass, wall, sky (see MS COCO Stuff),\npanoptic: full scene segmentation, with 80 thing categories (such as person, bicycle, elephant) and a subset of 91 stuff categories (grass, sky, road),\ndense pose: more than 39,000 images and 56,000 person instances labeled with DensePose annotations – each labeled person is annotated with an instance id and a mapping between image pixels that belong to that person body and a template 3D model.\nThe annotations are publicly available only for training and validation images." }, { "dkey": "CIFAR-10", "dval": "The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.\n\nThe criteria for deciding whether an image belongs to a class were as follows:\n\n\nThe class name should be high on the list of likely answers to the question “What is in this picture?”\nThe image should be photo-realistic. Labelers were instructed to reject line drawings.\nThe image should contain only one prominent instance of the object to which the class refers.\nThe object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler." }, { "dkey": "BraTS 2017", "dval": "The BRATS2017 dataset. It contains 285 brain tumor MRI scans, with four MRI modalities as T1, T1ce, T2, and Flair for each scan. The dataset also provides full masks for brain tumors, with labels for ED, ET, NET/NCR. The segmentation evaluation is based on three tasks: WT, TC and ET segmentation." }, { "dkey": "NAS-Bench-101", "dval": "NAS-Bench-101 is the first public architecture dataset for NAS research. To build NASBench-101, the authors carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional\narchitectures. The authors trained and evaluated all of these architectures multiple times on CIFAR-10 and compiled the results into a large dataset of over 5 million trained models. This allows researchers to evaluate the quality of a diverse range of models in milliseconds by querying the precomputed dataset." }, { "dkey": "Places", "dval": "The Places dataset is proposed for scene recognition and contains more than 2.5 million images covering more than 205 scene categories with more than 5,000 images per category." }, { "dkey": "GQA", "dval": "The GQA dataset is a large-scale visual question answering dataset with real images from the Visual Genome dataset and balanced question-answer pairs. Each training and validation image is also associated with scene graph annotations describing the classes and attributes of those objects in the scene, and their pairwise relations. Along with the images and question-answer pairs, the GQA dataset provides two types of pre-extracted visual features for each image – convolutional grid features of size 7×7×2048 extracted from a ResNet-101 network trained on ImageNet, and object detection features of size Ndet×2048 (where Ndet is the number of detected objects in each image with a maximum of 100 per image) from a Faster R-CNN detector." }, { "dkey": "KLEJ", "dval": "The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding task.\n\nKey benchmark features:\n\n\nIt contains a diverse set of tasks from different domains and with different objectives.\nMost tasks are created from existing datasets but the authors also released the new sentiment analysis dataset from an e-commerce domain.\nIt includes tasks which have relatively small datasets and require extensive external knowledge to solve them. It promotes the usage of transfer learning instead of training separate models from scratch.\n\nThe name KLEJ (English: GLUE) is an abbreviation for Kompleksowa Lista Ewaluacji Językowych (English: Comprehensive List of Language Evaluations) and refers to the GLUE benchmark." }, { "dkey": "COCO-Text", "dval": "The COCO-Text dataset is a dataset for text detection and recognition. It is based on the MS COCO dataset, which contains images of complex everyday scenes. The COCO-Text dataset contains non-text images, legible text images and illegible text images. In total there are 22184 training images and 7026 validation images with at least one instance of legible text." } ]

[ "Airport", "Partial-iLIDS", "CUHK02", "SYSU-MM01", "P-DESTRE", "Twitter100k" ]

[ "Market-1501", "MARS" ]

[ { "dkey": "Market-1501", "dval": "Market-1501 is a large-scale public benchmark dataset for person re-identification. It contains 1501 identities which are captured by six different cameras, and 32,668 pedestrian image bounding-boxes obtained using the Deformable Part Models pedestrian detector. Each person has 3.6 images on average at each viewpoint. The dataset is split into two parts: 750 identities are utilized for training and the remaining 751 identities are used for testing. In the official testing protocol 3,368 query images are selected as probe set to find the correct match across 19,732 reference gallery images." }, { "dkey": "MARS", "dval": "MARS (Motion Analysis and Re-identification Set) is a large scale video based person reidentification dataset, an extension of the Market-1501 dataset. It has been collected from six near-synchronized cameras. It consists of 1,261 different pedestrians, who are captured by at least 2 cameras. The variations in poses, colors and illuminations of pedestrians, as well as the poor image quality, make it very difficult to yield high matching accuracy. Moreover, the dataset contains 3,248 distractors in order to make it more realistic. Deformable Part Model and GMMCP tracker were used to automatically generate the tracklets (mostly 25-50 frames long)." }, { "dkey": "Airport", "dval": "The Airport dataset is a dataset for person re-identification which consists of 39,902 images and 9,651 identities across six cameras." }, { "dkey": "Partial-iLIDS", "dval": "Partial iLIDS is a dataset for occluded person person re-identification. It contains a total of 476 images of 119 people captured by 4 non-overlapping cameras. Some images contain people occluded by other individuals or luggage." }, { "dkey": "CUHK02", "dval": "CUHK02 is a dataset for person re-identification. It contains 1,816 identities from two disjoint camera views. Each identity has two samples per camera view making a total of 7,264 images. It is used for Person Re-identification." }, { "dkey": "SYSU-MM01", "dval": "The SYSU-MM01 is a dataset collected for the Visible-Infrared Re-identification problem. The images in the dataset were obtained from 491 different persons by recording them using 4 RGB and 2 infrared cameras. Within the dataset, the persons are divided into 3 fixed splits to create training, validation and test sets. In the training set, there are 20284 RGB and 9929 infrared images of 296 persons. The validation set contains 1974 RGB and 1980 infrared images of 99 persons. The testing set consists of the images of 96 persons where 3803 infrared images are used as query and 301 randomly selected RGB images are used as gallery." }, { "dkey": "P-DESTRE", "dval": "Provides consistent ID annotations across multiple days, making it suitable for the extremely challenging problem of person search, i.e., where no clothing information can be reliably used. Apart this feature, the P-DESTRE annotations enable the research on UAV-based pedestrian detection, tracking, re-identification and soft biometric solutions." }, { "dkey": "Twitter100k", "dval": "Twitter100k is a large-scale dataset for weakly supervised cross-media retrieval." } ]