query
stringlengths 17
161
| keyphrase_query
stringlengths 3
85
| year
int64 2.01k
2.02k
| negative_cands
sequence | positive_cands
sequence | abstracts
list |
|---|---|---|---|---|---|
I want to implement a real-time action detection system. | action detection video | 2,017 | [
"NAB",
"G3D",
"ESAD",
"BAR",
"SoccerDB"
] | [
"UCF101",
"COCO"
] | [
{
"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": "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": "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": "G3D",
"dval": "The Gaming 3D Dataset (G3D) focuses on real-time action recognition in a gaming scenario. It contains 10 subjects performing 20 gaming actions: “punch right”, “punch left”, “kick right”, “kick left”, “defend”, “golf swing”, “tennis swing forehand”, “tennis swing backhand”, “tennis serve”, “throw bowling ball”, “aim and fire gun”, “walk”, “run”, “jump”, “climb”, “crouch”, “steer a car”, “wave”, “flap” and “clap”."
},
{
"dkey": "ESAD",
"dval": "ESAD is a large-scale dataset designed to tackle the problem of surgeon action detection in endoscopic minimally invasive surgery. ESAD aims at contributing to increase the effectiveness and reliability of surgical assistant robots by realistically testing their awareness of the actions performed by a surgeon. The dataset provides bounding box annotation for 21 action classes on real endoscopic video frames captured during prostatectomy, and was used as the basis of a recent MIDL 2020 challenge."
},
{
"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": "SoccerDB",
"dval": "Comprises of 171,191 video segments from 346 high-quality soccer games. The database contains 702,096 bounding boxes, 37,709 essential event labels with time boundary and 17,115 highlight annotations for object detection, action recognition, temporal action localization, and highlight detection tasks."
}
] |
We propose a deep learning based framework for image relighting. It consists of a generator network which | image relighting images | 2,019 | [
"AMASS",
"Places",
"GoPro",
"UNSW-NB15"
] | [
"CARLA",
"KITTI"
] | [
{
"dkey": "CARLA",
"dval": "CARLA (CAR Learning to Act) is an open simulator for urban driving, developed as an open-source layer over Unreal Engine 4. Technically, it operates similarly to, as an open source layer over Unreal Engine 4 that provides sensors in the form of RGB cameras (with customizable positions), ground truth depth maps, ground truth semantic segmentation maps with 12 semantic classes designed for driving (road, lane marking, traffic sign, sidewalk and so on), bounding boxes for dynamic objects in the environment, and measurements of the agent itself (vehicle location and orientation)."
},
{
"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": "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": "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": "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": "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"
}
] |
I want to select sentences to support my answers for the multi-hop questions. | multi-hop question answering text | 2,019 | [
"WikiHop",
"CommonsenseQA",
"HybridQA",
"GYAFC",
"BiPaR",
"QNLI",
"QED"
] | [
"ARC",
"MultiRC"
] | [
{
"dkey": "ARC",
"dval": "The AI2’s Reasoning Challenge (ARC) dataset is a multiple-choice question-answering dataset, containing questions from science exams from grade 3 to grade 9. The dataset is split in two partitions: Easy and Challenge, where the latter partition contains the more difficult questions that require reasoning. Most of the questions have 4 answer choices, with <1% of all the questions having either 3 or 5 answer choices. ARC includes a supporting KB of 14.3M unstructured text passages."
},
{
"dkey": "MultiRC",
"dval": "MultiRC (Multi-Sentence Reading Comprehension) is a dataset of short paragraphs and multi-sentence questions, i.e., questions that can be answered by combining information from multiple sentences of the paragraph.\nThe dataset was designed with three key challenges in mind:\n* The number of correct answer-options for each question is not pre-specified. This removes the over-reliance on answer-options and forces them to decide on the correctness of each candidate answer independently of others. In other words, the task is not to simply identify the best answer-option, but to evaluate the correctness of each answer-option individually.\n* The correct answer(s) is not required to be a span in the text.\n* The paragraphs in the dataset have diverse provenance by being extracted from 7 different domains such as news, fiction, historical text etc., and hence are expected to be more diverse in their contents as compared to single-domain datasets.\nThe entire corpus consists of around 10K questions (including about 6K multiple-sentence questions). The 60% of the data is released as training and development data. The rest of the data is saved for evaluation and every few months a new unseen additional data is included for evaluation to prevent unintentional overfitting over time."
},
{
"dkey": "WikiHop",
"dval": "WikiHop is a multi-hop question-answering dataset. The query of WikiHop is constructed with entities and relations from WikiData, while supporting documents are from WikiReading. A bipartite graph connecting entities and documents is first built and the answer for each query is located by traversal on this graph. Candidates that are type-consistent with the answer and share the same relation in query with the answer are included, resulting in a set of candidates. Thus, WikiHop is a multi-choice style reading comprehension data set. There are totally about 43K samples in training set, 5K samples in development set and 2.5K samples in test set. The test set is not provided. The task is to predict the correct answer given a query and multiple supporting documents.\n\nThe dataset includes a masked variant, where all candidates and their mentions in the supporting documents are replaced by random but consistent placeholder tokens."
},
{
"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": "HybridQA",
"dval": "A new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable."
},
{
"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": "BiPaR",
"dval": "BiPaR is a manually annotated bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support monolingual, multilingual and cross-lingual reading comprehension on novels. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written in parallel in two languages. BiPaR is diverse in prefixes of questions, answer types and relationships between questions and passages. Answering the questions requires reading comprehension skills of coreference resolution, multi-sentence reasoning, and understanding of implicit causality."
},
{
"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": "QED",
"dval": "QED is a linguistically principled framework for explanations in question answering. Given a question and a passage, QED represents an explanation of the answer as a combination of discrete, human-interpretable steps:\nsentence selection := identification of a sentence implying an answer to the question\nreferential equality := identification of noun phrases in the question and the answer sentence that refer to the same thing\npredicate entailment := confirmation that the predicate in the sentence entails the predicate in the question once referential equalities are abstracted away.\nThe QED dataset is an expert-annotated dataset of QED explanations build upon a subset of the Google Natural Questions dataset."
}
] |
This paper proposes a novel self-guiding LSTM (sg-LSTM) image caption | image captioning images text | 2,019 | [
"Bengali Hate Speech",
"Weibo NER",
"nocaps",
"AOLP",
"MSU-MFSD",
"MVSEC"
] | [
"COCO",
"Flickr30k"
] | [
{
"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": "Flickr30k",
"dval": "The Flickr30k dataset contains 31,000 images collected from Flickr, together with 5 reference sentences provided by human annotators."
},
{
"dkey": "Bengali Hate Speech",
"dval": "Introduces three datasets of expressing hate, commonly used topics, and opinions for hate speech detection, document classification, and sentiment analysis, respectively."
},
{
"dkey": "Weibo NER",
"dval": "The Weibo NER dataset is a Chinese Named Entity Recognition dataset drawn from the social media website Sina Weibo."
},
{
"dkey": "nocaps",
"dval": "The nocaps benchmark consists of 166,100 human-generated captions describing 15,100 images from the OpenImages validation and test sets."
},
{
"dkey": "AOLP",
"dval": "The application-oriented license plate (AOLP) benchmark database has 2049 images of Taiwan license plates. This database is categorized into three subsets: access control (AC) with 681 samples, traffic law enforcement (LE) with 757 samples, and road patrol (RP) with 611 samples. AC refers to the cases that a vehicle passes a fixed passage with a lower speed or full stop. This is the easiest situation. The images are captured under different illuminations and different weather conditions. LE refers to the cases that a vehicle violates traffic laws and is captured by roadside camera. The background are really cluttered, with road sign and multiple plates in one image. RP refers to the cases that the camera is held on a patrolling vehicle, and the images are taken with arbitrary viewpoints and distances."
},
{
"dkey": "MSU-MFSD",
"dval": "The MSU-MFSD dataset contains 280 video recordings of genuine and attack faces. 35 individuals have participated in the development of this database with a total of 280 videos. Two kinds of cameras with different resolutions (720×480 and 640×480) were used to record the videos from the 35 individuals. For the real accesses, each individual has two video recordings captured with the Laptop cameras and Android, respectively. For the video attacks, two types of cameras, the iPhone and Canon cameras were used to capture high definition videos on each of the subject. The videos taken with Canon camera were then replayed on iPad Air screen to generate the HD replay attacks while the videos recorded by the iPhone mobile were replayed itself to generate the mobile replay attacks. Photo attacks were produced by printing the 35 subjects’ photos on A3 papers using HP colour printer. The recording videos with respect to the 35 individuals were divided into training (15 subjects with 120 videos) and testing (40 subjects with 160 videos) datasets, respectively."
},
{
"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."
}
] |
I want to train a model to find the referred object within the image according to the natural | natural language object retrieval images paragraph-level | 2,017 | [
"SNIPS",
"COVERAGE",
"ConvAI2",
"Image and Video Advertisements",
"Market-1501",
"CLEVR-Hans"
] | [
"COCO",
"ReferItGame"
] | [
{
"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": "ReferItGame",
"dval": "The ReferIt dataset contains 130,525 expressions for referring to 96,654 objects in 19,894 images of natural scenes."
},
{
"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": "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": "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)."
},
{
"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": "CLEVR-Hans",
"dval": "The CLEVR-Hans data set is a novel confounded visual scene data set, which captures complex compositions of different objects. This data set consists of CLEVR images divided into several classes. \n\nThe membership of a class is based on combinations of objects’ attributes and relations. Additionally, certain classes within the data set are confounded. Thus, within the data set, consisting of train, validation, and test splits, all train, and validation images of confounded classes will be confounded with a specific attribute or combination of attributes.\n\nEach class is represented by 3000 training images, 750 validation images, and 750 test images. The training, validation, and test set splits contain 9000, 2250, and 2250 samples, respectively, for CLEVR-Hans3 and 21000, 5250, and 5250 samples for CLEVR-Hans7. The class distribution is balanced for all data splits.\n\nFor CLEVR-Hans classes for which class rules contain more than three objects, the number of objects to be placed per scene was randomly chosen between the minimal required number of objects for that class and ten, rather than between three and ten, as in the original CLEVR data set.\n\nFinally, the images were created such that the exact combinations of the class rules did not occur in images of other classes. It is possible that a subset of objects from one class rule occur in an image of another class. However, it is not possible that more than one complete class rule is contained in an image."
}
] |
This is the source code of my paper. | language modeling | 2,019 | [
"CommonsenseQA",
"SNIPS",
"PHINC",
"ConvAI2",
"CONCODE"
] | [
"WebText",
"WikiText-103"
] | [
{
"dkey": "WebText",
"dval": "WebText is an internal OpenAI corpus created by scraping web pages with emphasis on\ndocument quality. The authors scraped all outbound links from\nReddit which received at least 3\nkarma. The authors used the approach as a heuristic indicator for\nwhether other users found the link interesting, educational,\nor just funny.\n\nWebText contains the text subset of these 45 million links. It consists of over 8 million documents\nfor a total of 40 GB of text. All Wikipedia\ndocuments were removed from WebText since it is a common data source\nfor other datasets."
},
{
"dkey": "WikiText-103",
"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": "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": "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": "PHINC",
"dval": "PHINC is a parallel corpus of the 13,738 code-mixed English-Hindi sentences and their corresponding translation in English. The translations of sentences are done manually by the annotators."
},
{
"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": "CONCODE",
"dval": "A new large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment."
}
] |
We propose an end-to-end model for cross-lingual transfer learning for question answering. We | question answering text | 2,019 | [
"iVQA",
"ReQA",
"EXAMS",
"XQA",
"XQuAD"
] | [
"DRCD",
"NewsQA",
"SQuAD"
] | [
{
"dkey": "DRCD",
"dval": "Delta Reading Comprehension Dataset (DRCD) is an open domain traditional Chinese machine reading comprehension (MRC) dataset. This dataset aimed to be a standard Chinese machine reading comprehension dataset, which can be a source dataset in transfer learning. The dataset contains 10,014 paragraphs from 2,108 Wikipedia articles and 30,000+ questions generated by annotators."
},
{
"dkey": "NewsQA",
"dval": "The NewsQA dataset is a crowd-sourced machine reading comprehension dataset of 120,000 question-answer pairs.\n\n\nDocuments are CNN news articles.\nQuestions are written by human users in natural language.\nAnswers may be multiword passages of the source text.\nQuestions may be unanswerable.\nNewsQA is collected using a 3-stage, siloed process.\nQuestioners see only an article’s headline and highlights.\nAnswerers see the question and the full article, then select an answer passage.\nValidators see the article, the question, and a set of answers that they rank.\nNewsQA is more natural and more challenging than previous datasets."
},
{
"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": "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": "ReQA",
"dval": "Retrieval Question-Answering (ReQA) benchmark tests a model’s ability to retrieve relevant answers efficiently from a large set of documents."
},
{
"dkey": "EXAMS",
"dval": "A new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. Collects more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. EXAMS offers a fine-grained evaluation framework across multiple languages and subjects, which allows precise analysis and comparison of various models."
},
{
"dkey": "XQA",
"dval": "XQA is a data which consists of a total amount of 90k question-answer pairs in nine languages for cross-lingual open-domain question answering."
},
{
"dkey": "XQuAD",
"dval": "XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently, the dataset is entirely parallel across 11 languages."
}
] |
The proposed model can learn to disentangle appearance and geometric information from image and video sequences in | image/video editing | 2,018 | [
"Moving MNIST",
"irc-disentanglement",
"REDS",
"3DMatch",
"ABC Dataset",
"MAFL"
] | [
"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": "Moving MNIST",
"dval": "The Moving MNIST dataset contains 10,000 video sequences, each consisting of 20 frames. In each video sequence, two digits move independently around the frame, which has a spatial resolution of 64×64 pixels. The digits frequently intersect with each other and bounce off the edges of the frame"
},
{
"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": "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": "3DMatch",
"dval": "The 3DMATCH benchmark evaluates how well descriptors (both 2D and 3D) can establish correspondences between RGB-D frames of different views. The dataset contains 2D RGB-D patches and 3D patches (local TDF voxel grid volumes) of wide-baselined correspondences. \n\nThe pixel size of each 2D patch is determined by the projection of the 0.3m3 local 3D patch around the interest point onto the image plane."
},
{
"dkey": "ABC Dataset",
"dval": "The ABC Dataset is a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms."
},
{
"dkey": "MAFL",
"dval": "The MAFL dataset contains manually annotated facial landmark locations for 19,000 training and 1,000 test images."
}
] |
A novel cascaded CNN scheme for accurate face landmark localization. | face landmark localization images | 2,018 | [
"WFLW",
"AFLW2000-3D",
"UTKFace",
"LS3D-W",
"LaPa"
] | [
"Helen",
"AFW"
] | [
{
"dkey": "Helen",
"dval": "The HELEN dataset is composed of 2330 face images of 400×400 pixels with labeled facial components generated through manually-annotated contours along eyes, eyebrows, nose, lips and jawline."
},
{
"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": "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": "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": "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": "LS3D-W",
"dval": "A 3D facial landmark dataset of around 230,000 images."
},
{
"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."
}
] |
We propose a unified model that combines the strengths of two well-established deformable model approaches to the face alignment | face alignment images | 2,015 | [
"iFakeFaceDB",
"PANDORA",
"MaskedFace-Net",
"SpeakingFaces",
"EPIC-KITCHENS-100",
"Scan2CAD"
] | [
"AFW",
"LFPW"
] | [
{
"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": "LFPW",
"dval": "The Labeled Face Parts in-the-Wild (LFPW) consists of 1,432 faces from images downloaded from the web using simple text queries on sites such as google.com, flickr.com, and yahoo.com. Each image was labeled by three MTurk workers, and 29 fiducial points, shown below, are included in dataset."
},
{
"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": "PANDORA",
"dval": "PANDORA is the first large-scale dataset of Reddit comments labeled with three personality models (including the well-established Big 5 model) and demographics (age, gender, and location) for more than 10k users."
},
{
"dkey": "MaskedFace-Net",
"dval": "Proposes three types of masked face detection dataset; namely, the Correctly Masked Face Dataset (CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for the global masked face detection (MaskedFace-Net)."
},
{
"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": "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": "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."
}
] |
We report the results of our replication study on BERT pretraining. Our best model outperforms every published | language model pretraining text | 2,019 | [
"GSL",
"THEODORE",
"ReCAM",
"BDD100K",
"Horne 2017 Fake News Data"
] | [
"QNLI",
"MRPC",
"RACE",
"GLUE",
"SQuAD"
] | [
{
"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": "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": "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": "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": "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": "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": "ReCAM",
"dval": "Tasks\nOur shared task has three subtasks. Subtask 1 and 2 focus on evaluating machine learning models' performance with regard to two definitions of abstractness (Spreen and Schulz, 1966; Changizi, 2008), which we call imperceptibility and nonspecificity, respectively. Subtask 3 aims to provide some insights to their relationships.\n\n• Subtask 1: ReCAM-Imperceptibility\n\nConcrete words refer to things, events, and properties that we can perceive directly with our senses (Spreen and Schulz, 1966; Coltheart 1981; Turney et al., 2011), e.g., donut, trees, and red. In contrast, abstract words refer to ideas and concepts that are distant from immediate perception. Examples include objective, culture, and economy. In subtask 1, the participanting systems are required to perform reading comprehension of abstract meaning for imperceptible concepts.\n\nBelow is an example. Given a passage and a question, your model needs to choose from the five candidates the best one for replacing @placeholder.\n\n• Subtask 2: ReCAM-Nonspecificity\n\nSubtask 2 focuses on a different type of definition. Compared to concrete concepts like groundhog and whale, hypernyms such as vertebrate are regarded as more abstract (Changizi, 2008). \n\n• Subtask 3: ReCAM-Intersection\nSubtask 3 aims to provide more insights to the relationship of the two views on abstractness, In this subtask, we test the performance of a system that is trained on one definition and evaluted on the other."
},
{
"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": "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"
}
] |
We present a simple and effective model for learning general purpose sentence representations. Our model uses a single | natural language inference text | 2,017 | [
"GLUE",
"SuperGLUE",
"Fluent Speech Commands",
"BDD100K"
] | [
"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": "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": "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": "Fluent Speech Commands",
"dval": "Fluent Speech Commands is an open source audio dataset for spoken language understanding (SLU) experiments. Each utterance is labeled with \"action\", \"object\", and \"location\" values; for example, \"turn the lights on in the kitchen\" has the label {\"action\": \"activate\", \"object\": \"lights\", \"location\": \"kitchen\"}. A model must predict each of these values, and a prediction for an utterance is deemed to be correct only if all values are correct. \n\nThe task is very simple, but the dataset is large and flexible to allow for many types of experiments: for instance, one can vary the number of speakers, or remove all instances of a particular sentence and test whether a model trained on the remaining sentences can generalize."
},
{
"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."
}
] |
This paper proposes a new multiple-choice reading comprehension (MCRC) model which performs | multiple-choice reading comprehension text paragraph-level | 2,019 | [
"DREAM",
"DROP",
"CosmosQA",
"OneStopQA",
"C3",
"VisualMRC"
] | [
"RACE",
"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": "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": "DREAM",
"dval": "DREAM is a multiple-choice Dialogue-based REAding comprehension exaMination dataset. In contrast to existing reading comprehension datasets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding.\n\nDREAM contains 10,197 multiple choice questions for 6,444 dialogues, collected from English-as-a-foreign-language examinations designed by human experts. DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge."
},
{
"dkey": "DROP",
"dval": "Discrete Reasoning Over Paragraphs DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was necessary for prior datasets. The questions consist of passages extracted from Wikipedia articles. The dataset is split into a training set of about 77,000 questions, a development set of around 9,500 questions and a hidden test set similar in size to the development set."
},
{
"dkey": "CosmosQA",
"dval": "CosmosQA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people’s everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context."
},
{
"dkey": "OneStopQA",
"dval": "OneStopQA provides an alternative test set for reading comprehension which alleviates these shortcomings and has a substantially higher human ceiling performance."
},
{
"dkey": "C3",
"dval": "C3 is a free-form multiple-Choice Chinese machine reading Comprehension dataset."
},
{
"dkey": "VisualMRC",
"dval": "VisualMRC is a visual machine reading comprehension dataset that proposes a task: given a question and a document image, a model produces an abstractive answer.\n\nYou can find more details, analyses, and baseline results in the paper, \nVisualMRC: Machine Reading Comprehension on Document Images, AAAI 2021.\n\nStatistics:\n10,197 images\n30,562 QA pairs\n10.53 average question tokens (tokenizing with NLTK tokenizer)\n9.53 average answer tokens (tokenizing wit NLTK tokenizer)\n151.46 average OCR tokens (tokenizing with NLTK tokenizer)"
}
] |
I have been reading about blood vessel segmentation and tried to reproduce the results. | retinal blood vessel segmentation images | 2,017 | [
"IntrA",
"COCO-Tasks",
"ORVS",
"SUN3D",
"ROSE"
] | [
"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": "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."
},
{
"dkey": "COCO-Tasks",
"dval": "Comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated."
},
{
"dkey": "ORVS",
"dval": "The ORVS dataset has been newly established as a collaboration between the computer science and visual-science departments at the University of Calgary.\n\nThis dataset contains 49 images (42 training and seven testing images) collected from a clinic in Calgary-Canada. All images were acquired with a Zeiss Visucam 200 with 30 degrees field of view (FOV). The image size is 1444×1444 with 24 bits per pixel. Images and are stored in JPEG format with low compression, which is common in ophthalmology practice. All images were manually traced by an expert who a has been working in the field of retinal-image analysis and went through training. The expert was asked to label all pixels belonging to retinal vessels. The Windows Paint 3D tool was used to manually label the images."
},
{
"dkey": "SUN3D",
"dval": "SUN3D contains a large-scale RGB-D video database, with 8 annotated sequences. Each frame has a semantic segmentation of the objects in the scene and information about the camera pose. It is composed by 415 sequences captured in 254 different spaces, in 41 different buildings. Moreover, some places have been captured multiple times at different moments of the day."
},
{
"dkey": "ROSE",
"dval": "Retinal OCTA SEgmentation dataset (ROSE) consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level."
}
] |
I want to build a model to automatically determine whether an image is acceptable for diagnosis. | fundus image quality classification images | 2,018 | [
"ACDC",
"SemEval 2014 Task 4 Sub Task 2",
"QNLI",
"Image and Video Advertisements",
"IntrA",
"Violin"
] | [
"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": "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": "SemEval 2014 Task 4 Sub Task 2",
"dval": "Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e.g., laptops, restaurants) and their aspects (e.g., battery, screen; food, service). By contrast, this task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect. Datasets consisting of customer reviews with human-authored annotations identifying the mentioned aspects of the target entities and the sentiment polarity of each aspect will be provided.\n\nSubtask 2: Aspect term polarity\n\nFor a given set of aspect terms within a sentence, determine whether the polarity of each aspect term is positive, negative, neutral or conflict (i.e., both positive and negative).\n\nFor example:\n\n“I loved their fajitas” → {fajitas: positive}\n“I hated their fajitas, but their salads were great” → {fajitas: negative, salads: positive}\n“The fajitas are their first plate” → {fajitas: neutral}\n“The fajitas were great to taste, but not to see” → {fajitas: conflict}"
},
{
"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": "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": "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."
},
{
"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."
}
] |
We propose an end-to-end framework to reconstruct the 3D scene from | semantic reconstruction indoor scenes images | 2,020 | [
"DIPS",
"MLe2e",
"E2E",
"DeeperForensics-1.0",
"THEODORE",
"DDD20"
] | [
"COCO",
"Pix3D"
] | [
{
"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": "Pix3D",
"dval": "The Pix3D dataset is a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc."
},
{
"dkey": "DIPS",
"dval": "Contains biases but is two orders of magnitude larger than those used previously."
},
{
"dkey": "MLe2e",
"dval": "MLe2 is a dataset for the evaluation of scene text end-to-end reading systems and all intermediate stages such as text detection, script identification and text recognition. The dataset contains a total of 711 scene images covering four different scripts (Latin, Chinese, Kannada, and Hangul)."
},
{
"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": "DeeperForensics-1.0",
"dval": "DeeperForensics-1.0 represents the largest face forgery detection dataset by far, with 60,000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind. The full dataset includes 48,475 source videos and 11,000 manipulated videos. The source videos are collected on 100 paid and consented actors from 26 countries, and the manipulated videos are generated by a newly proposed many-to-many end-to-end face swapping method, DF-VAE. 7 types of real-world perturbations at 5 intensity levels are employed to ensure a larger scale and higher diversity."
},
{
"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": "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."
}
] |
We investigate the effectiveness of different pre-trained language models for Question Answering (QA) on four | question answering text | 2,019 | [
"How2QA",
"TweetQA",
"SQuAD-shifts",
"PAQ",
"TVQA"
] | [
"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": "How2QA",
"dval": "To collect How2QA for video QA task, the same set of selected video clips are presented to another group of AMT workers for multichoice QA annotation. Each worker is assigned with one video segment and asked to write one question with four answer candidates (one correctand three distractors). Similarly, narrations are hidden from the workers to ensure the collected QA pairs are not biased by subtitles. Similar to TVQA, the start and end points are provided for the relevant moment for each question. After filtering low-quality annotations, the final dataset contains 44,007 QA pairs for 22k 60-second clips selected from 9035 videos."
},
{
"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": "SQuAD-shifts",
"dval": "Provides four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data."
},
{
"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": "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."
}
] |
I want to use a supervised model to recognize activities from low-resolution videos. | extreme low-resolution activity recognition images | 2,019 | [
"TinyVIRAT",
"DAiSEE",
"DIV2K",
"UCF-Crime",
"MPII Cooking 2 Dataset",
"Composable activities dataset",
"FaceForensics"
] | [
"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": "TinyVIRAT",
"dval": "TinyVIRAT contains natural low-resolution activities. The actions in TinyVIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging."
},
{
"dkey": "DAiSEE",
"dval": "DAiSEE is a multi-label video classification dataset comprising of 9,068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration \"in the wild\". The dataset has four levels of labels namely - very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists."
},
{
"dkey": "DIV2K",
"dval": "DIV2K is a popular single-image super-resolution dataset which contains 1,000 images with different scenes and is splitted to 800 for training, 100 for validation and 100 for testing. It was collected for NTIRE2017 and NTIRE2018 Super-Resolution Challenges in order to encourage research on image super-resolution with more realistic degradation. This dataset contains low resolution images with different types of degradations. Apart from the standard bicubic downsampling, several types of degradations are considered in synthesizing low resolution images for different tracks of the challenges. Track 2 of NTIRE 2017 contains low resolution images with unknown x4 downscaling. Track 2 and track 4 of NTIRE 2018 correspond to realistic mild ×4 and realistic wild ×4 adverse conditions, respectively. Low-resolution images under realistic mild x4 setting suffer from motion blur, Poisson noise and pixel shifting. Degradations under realistic wild x4 setting are further extended to be of different levels from image to image."
},
{
"dkey": "UCF-Crime",
"dval": "The UCF-Crime dataset is a large-scale dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. These anomalies are selected because they have a significant impact on public safety. \n\nThis dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities."
},
{
"dkey": "MPII Cooking 2 Dataset",
"dval": "A dataset which provides detailed annotations for activity recognition."
},
{
"dkey": "Composable activities dataset",
"dval": "The Composable activities dataset consists of 693 videos that contain activities in 16 classes performed by 14 actors. Each activity is composed of 3 to 11 atomic actions. RGB-D data for each sequence is captured using a Microsoft Kinect sensor and estimate position of relevant body joints.\n\nThe dataset provides annotations of the activity for each video and the actions for each of the four human parts (left/right arm and leg) for each frame in every video."
},
{
"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."
}
] |
I want to learn an action recognition model from trimmed videos. | action recognition videos | 2,019 | [
"Kinetics-600",
"Kinetics",
"AViD",
"DISFA",
"JHMDB",
"MTL-AQA",
"EPIC-KITCHENS-100"
] | [
"UCF101",
"ActivityNet"
] | [
{
"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": "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": "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": "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": "AViD",
"dval": "Is a collection of action videos from many different countries. The motivation is to create a public dataset that would benefit training and pretraining of action recognition models for everybody, rather than making it useful for limited countries."
},
{
"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": "JHMDB",
"dval": "JHMDB is an action recognition dataset that consists of 960 video sequences belonging to 21 actions. It is a subset of the larger HMDB51 dataset collected from digitized movies and YouTube videos. The dataset contains video and annotation for puppet flow per frame (approximated optimal flow on the person), puppet mask per frame, joint positions per frame, action label per clip and meta label per clip (camera motion, visible body parts, camera viewpoint, number of people, video quality)."
},
{
"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": "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."
}
] |
I want to train a supervised model that is robust to adversarial perturbations. | adversarial robustness image classification | 2,018 | [
"ImageNet-P",
"NYU-VP",
"eQASC",
"SNIPS",
"DailyDialog++",
"APRICOT",
"Clothing1M"
] | [
"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": "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."
},
{
"dkey": "NYU-VP",
"dval": "NYU-VP is a new dataset for multi-model fitting, vanishing point (VP) estimation in this case. Each image is annotated with up to eight vanishing points, and pre-extracted line segments are provided which act as data points for a robust estimator. Due to its size, the dataset is the first to allow for supervised learning of a multi-model fitting task."
},
{
"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": "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": "DailyDialog++",
"dval": "Consists of (i) five relevant responses for each context and (ii) five adversarially crafted irrelevant responses for each context."
},
{
"dkey": "APRICOT",
"dval": "APRICOT is a collection of over 1,000 annotated photographs of printed adversarial patches in public locations. The patches target several object categories for three COCO-trained detection models, and the photos represent natural variation in position, distance, lighting conditions, and viewing angle."
},
{
"dkey": "Clothing1M",
"dval": "Clothing1M contains 1M clothing images in 14 classes. It is a dataset with noisy labels, since the data is collected from several online shopping websites and include many mislabelled samples. This dataset also contains 50k, 14k, and 10k images with clean labels for training, validation, and testing, respectively."
}
] |
We propose a simple yet effective approach to exploit the available dense depth | 3d semantic labeling images dense depth maps outdoor street scenes | 2,018 | [
"DocBank",
"IMDB-BINARY",
"REDDIT-BINARY",
"Localized Narratives",
"SBU Captions Dataset",
"Shiny dataset"
] | [
"SYNTHIA",
"Cityscapes"
] | [
{
"dkey": "SYNTHIA",
"dval": "The SYNTHIA dataset is a synthetic dataset that consists of 9400 multi-viewpoint photo-realistic frames rendered from a virtual city and comes with pixel-level semantic annotations for 13 classes. Each frame has resolution of 1280 × 960."
},
{
"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": "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": "IMDB-BINARY",
"dval": "IMDB-BINARY is a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie. These graphs are derived from the Action and Romance genres."
},
{
"dkey": "REDDIT-BINARY",
"dval": "REDDIT-BINARY consists of graphs corresponding to online discussions on Reddit. In each graph, nodes represent users, and there is an edge between them if at least one of them respond to the other’s comment. There are four popular subreddits, namely, IAmA, AskReddit, TrollXChromosomes, and atheism. IAmA and AskReddit are two question/answer based subreddits, and TrollXChromosomes and atheism are two discussion-based subreddits. A graph is labeled according to whether it belongs to a question/answer-based community or a discussion-based community."
},
{
"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": "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": "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)."
}
] |
A novel hybrid convolutional and transformer model, WaLDORf, that achieves state-of-the- | nlu text | 2,019 | [
"BraTS 2017",
"THEODORE",
"Glint360K",
"GTEA",
"PG-19",
"LibriSpeech",
"Multi-PIE"
] | [
"QNLI",
"GLUE",
"SQuAD"
] | [
{
"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": "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": "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": "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": "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": "GTEA",
"dval": "The Georgia Tech Egocentric Activities (GTEA) dataset contains seven types of daily activities such as making sandwich, tea, or coffee. Each activity is performed by four different people, thus totally 28 videos. For each video, there are about 20 fine-grained action instances such as take bread, pour ketchup, in approximately one minute."
},
{
"dkey": "PG-19",
"dval": "A new open-vocabulary language modelling benchmark derived from books."
},
{
"dkey": "LibriSpeech",
"dval": "The LibriSpeech corpus is a collection of approximately 1,000 hours of audiobooks that are a part of the LibriVox project. Most of the audiobooks come from the Project Gutenberg. The training data is split into 3 partitions of 100hr, 360hr, and 500hr sets while the dev and test data are split into the ’clean’ and ’other’ categories, respectively, depending upon how well or challenging Automatic Speech Recognition systems would perform against. Each of the dev and test sets is around 5hr in audio length. This corpus also provides the n-gram language models and the corresponding texts excerpted from the Project Gutenberg books, which contain 803M tokens and 977K unique words."
},
{
"dkey": "Multi-PIE",
"dval": "The Multi-PIE (Multi Pose, Illumination, Expressions) dataset consists of face images of 337 subjects taken under different pose, illumination and expressions. The pose range contains 15 discrete views, capturing a face profile-to-profile. Illumination changes were modeled using 19 flashlights located in different places of the room."
}
] |
Visual question answering (VQA) is an important task in the field of computer | visual question answering images natural language | 2,016 | [
"VizWiz",
"ST-VQA",
"VQA-E",
"TDIUC"
] | [
"DBpedia",
"COCO",
"DAQUAR"
] | [
{
"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": "DAQUAR",
"dval": "DAQUAR (DAtaset for QUestion Answering on Real-world images) is a dataset of human question answer pairs about images."
},
{
"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": "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": "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": "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."
}
] |
I want to build an effective tracking model based on a simple tracking framework. | tracking image sequences | 2,019 | [
"SNIPS",
"ProPara",
"Frames Dataset",
"PoseTrack"
] | [
"Penn Treebank",
"OTB"
] | [
{
"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": "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": "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": "ProPara",
"dval": "The ProPara dataset is designed to train and test comprehension of simple paragraphs describing processes (e.g., photosynthesis), designed for the task of predicting, tracking, and answering questions about how entities change during the process.\n\nProPara aims to promote the research in natural language understanding in the context of procedural text. This requires identifying the actions described in the paragraph and tracking state changes happening to the entities involved. The comprehension task is treated as that of predicting, tracking, and answering questions about how entities change during the procedure. The dataset contains 488 paragraphs and 3,300 sentences. Each paragraph is richly annotated with the existence and locations of all the main entities (the “participants”) at every time step (sentence) throughout the procedure (~81,000 annotations).\n\nProPara paragraphs are natural (authored by crowdsourcing) rather than synthetic (e.g., in bAbI). Workers were given a prompt (e.g., “What happens during photosynthesis?”) and then asked to author a series of sentences describing the sequence of events in the procedure. From these sentences, participant entities and their existence and locations were identified. The goal of the challenge is to predict the existence and location of each participant, based on sentences in the paragraph."
},
{
"dkey": "Frames Dataset",
"dval": "This dataset is dialog dataset collected in a Wizard-of-Oz fashion. Two humans talked to each other via a chat interface. One was playing the role of the user and the other one was playing the role of the conversational agent. The latter is called a wizard as a reference to the Wizard of Oz, the man behind the curtain. The wizards had access to a database of 250+ packages, each composed of a hotel and round-trip flights. The users were asked to find the best deal. This resulted in complex dialogues where a user would often consider different options, compare packages, and progressively build the description of her ideal trip."
},
{
"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."
}
] |
I want to use distant supervision to extract evidence sentences from reference documents for MRC tasks. | machine reading comprehension text paragraph-level | 2,019 | [
"DocRED",
"Delicious",
"ELI5",
"Melinda",
"DWIE",
"FOBIE"
] | [
"RACE",
"SearchQA",
"MultiNLI"
] | [
{
"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": "SearchQA",
"dval": "SearchQA was built using an in-production, commercial search engine. It closely reflects the full pipeline of a (hypothetical) general question-answering system, which consists of information retrieval and answer synthesis."
},
{
"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": "DocRED",
"dval": "DocRED (Document-Level Relation Extraction Dataset) is a relation extraction dataset constructed from Wikipedia and Wikidata. Each document in the dataset is human-annotated with named entity mentions, coreference information, intra- and inter-sentence relations, and supporting evidence. DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document. Along with the human-annotated data, the dataset provides large-scale distantly supervised data.\n\nDocRED contains 132,375 entities and 56,354 relational facts annotated on 5,053 Wikipedia documents. In addition to the human-annotated data, the dataset provides large-scale distantly supervised data over 101,873 documents."
},
{
"dkey": "Delicious",
"dval": "Delicious : This data set contains tagged web pages retrieved from the website delicious.com."
},
{
"dkey": "ELI5",
"dval": "ELI5 is a dataset for long-form question answering. It contains 270K complex, diverse questions that require explanatory multi-sentence answers. Web search results are used as evidence documents to answer each question.\n\nELI5 is also a task in Dodecadialogue."
},
{
"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": "DWIE",
"dval": "The 'Deutsche Welle corpus for Information Extraction' (DWIE) is a multi-task dataset that combines four main Information Extraction (IE) annotation sub-tasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document."
},
{
"dkey": "FOBIE",
"dval": "The Focused Open Biology Information Extraction (FOBIE) dataset aims to support IE from Computer-Aided Biomimetics. The dataset contains ~1,500 sentences from scientific biological texts. These sentences are annotated with TRADE-OFFS and syntactically similar relations between unbounded arguments, as well as argument-modifiers.\n\nThe FOBIE dataset has been used to explore Semi-Open Relation Extraction (SORE). The code for this and instructions can be found inside the SORE folder Readme.md, or in the ReadTheDocs documentations."
}
] |
I want to train a classifier to classify objects in images. | object classification images | 2,018 | [
"GYAFC",
"UCF101",
"Chinese Classifier",
"Food-101",
"SNIPS",
"StreetStyle"
] | [
"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": "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": "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": "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": "Food-101",
"dval": "The Food-101 dataset consists of 101 food categories with 750 training and 250 test images per category, making a total of 101k images. The labels for the test images have been manually cleaned, while the training set contains some noise."
},
{
"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": "StreetStyle",
"dval": "StreetStyle is a large-scale dataset of photos of people annotated with clothing attributes, and use this dataset to train attribute classifiers via deep learning."
}
] |
I want to use a video inpainting model to inpaint the frames of | video frame inpainting | 2,020 | [
"FVI",
"DeepFashion",
"DTD",
"OpenEDS",
"SNIPS",
"FaceForensics"
] | [
"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": "FVI",
"dval": "The Free-Form Video Inpainting dataset is a dataset used for training and evaluation video inpainting models. It consists of 1940 videos from the YouTube-VOS dataset and 12,600 videos from the YouTube-BoundingBoxes."
},
{
"dkey": "DeepFashion",
"dval": "DeepFashion is a dataset containing around 800K diverse fashion images with their rich annotations (46 categories, 1,000 descriptive attributes, bounding boxes and landmark information) ranging from well-posed product images to real-world-like consumer photos."
},
{
"dkey": "DTD",
"dval": "The Describable Textures Dataset (DTD) contains 5640 texture images in the wild. They are annotated with human-centric attributes inspired by the perceptual properties of textures."
},
{
"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": "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."
}
] |
An end-to-end hierarchical action recognition architecture. | action recognition video | 2,017 | [
"EPIC-KITCHENS-55",
"CCPD",
"E2E",
"PixelHelp",
"MLe2e",
"RCTW-17",
"DDD20"
] | [
"ImageNet",
"HMDB51"
] | [
{
"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": "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-55",
"dval": "The EPIC-KITCHENS-55 dataset comprises a set of 432 egocentric videos recorded by 32 participants in their kitchens at 60fps with a head mounted camera. There is no guiding script for the participants who freely perform activities in kitchens related to cooking, food preparation or washing up among others. Each video is split into short action segments (mean duration is 3.7s) with specific start and end times and a verb and noun annotation describing the action (e.g. ‘open fridge‘). The verb classes are 125 and the noun classes 331. The dataset is divided into one train and two test splits."
},
{
"dkey": "CCPD",
"dval": "The Chinese City Parking Dataset (CCPD) is a dataset for license plate detection and recognition. It contains over 250k unique car images, with license plate location annotations."
},
{
"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": "PixelHelp",
"dval": "PixelHelp includes 187 multi-step instructions of 4 task categories deined in https://support.google.com/pixelphone and annotated by human. This dataset includes 88 general tasks, such as configuring accounts, 38 Gmail tasks, 31 Chrome tasks, and 30 Photos related tasks. This dataset is an updated opensource version of the original PixelHelp dataset, which was used for testing the end-to-end grounding quality of the model in paper \"Mapping Natural Language Instructions to Mobile UI Action Sequences\". The similar accuracy is acquired on this version of the dataset."
},
{
"dkey": "MLe2e",
"dval": "MLe2 is a dataset for the evaluation of scene text end-to-end reading systems and all intermediate stages such as text detection, script identification and text recognition. The dataset contains a total of 711 scene images covering four different scripts (Latin, Chinese, Kannada, and Hangul)."
},
{
"dkey": "RCTW-17",
"dval": "Features a large-scale dataset with 12,263 annotated images. Two tasks, namely text localization and end-to-end recognition, are set up. The competition took place from January 20 to May 31, 2017. 23 valid submissions were received from 19 teams."
},
{
"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."
}
] |
I'm training a question answering system on the SQuAD dataset. | long-form question answering text paragraph-level | 2,019 | [
"Spoken-SQuAD",
"SQuAD",
"SQuAD-shifts",
"MultiReQA",
"TweetQA",
"QNLI"
] | [
"ELI5",
"WikiSum"
] | [
{
"dkey": "ELI5",
"dval": "ELI5 is a dataset for long-form question answering. It contains 270K complex, diverse questions that require explanatory multi-sentence answers. Web search results are used as evidence documents to answer each question.\n\nELI5 is also a task in Dodecadialogue."
},
{
"dkey": "WikiSum",
"dval": "WikiSum is a dataset based on English Wikipedia and suitable for a task of multi-document abstractive summarization. In each instance, the input is comprised of a Wikipedia topic (title of article) and a collection of non-Wikipedia reference documents, and the target is the Wikipedia article text. The dataset is restricted to the articles with at least one crawlable citation. The official split divides the articles roughly into 80/10/10 for train/development/test subsets, resulting in 1865750, 233252, and 232998 examples respectively."
},
{
"dkey": "Spoken-SQuAD",
"dval": "In SpokenSQuAD, the document is in spoken form, the input question is in the form of text and the answer to each question is always a span in the document. The following procedures were used to generate spoken documents from the original SQuAD dataset. First, the Google text-to-speech system was used to generate the spoken version of the articles in SQuAD. Then CMU Sphinx was sued to generate the corresponding ASR transcriptions. The SQuAD training set was used to generate the training set of Spoken SQuAD, and SQuAD development set was used to generate the testing set for Spoken SQuAD. If the answer of a question did not exist in the ASR transcriptions of the associated article, the question-answer pair was removed from the dataset because these examples are too difficult for listening comprehension machine at this stage."
},
{
"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": "SQuAD-shifts",
"dval": "Provides four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data."
},
{
"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": "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": "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."
}
] |
A novel CNN architecture for face detection. The main contribution is a new loss layer for CNNs, which | face detection image | 2,016 | [
"MMED",
"THEODORE",
"AFLW2000-3D",
"MSU-MFSD",
"CNN/Daily Mail",
"ReCoRD",
"MLSUM"
] | [
"COFW",
"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": "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": "MMED",
"dval": "Contains 25,165 textual news articles collected from hundreds of news media sites (e.g., Yahoo News, Google News, CNN News.) and 76,516 image posts shared on Flickr social media, which are annotated according to 412 real-world events. The dataset is collected to explore the problem of organizing heterogeneous data contributed by professionals and amateurs in different data domains, and the problem of transferring event knowledge obtained from one data domain to heterogeneous data domain, thus summarizing the data with different contributors."
},
{
"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": "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": "MSU-MFSD",
"dval": "The MSU-MFSD dataset contains 280 video recordings of genuine and attack faces. 35 individuals have participated in the development of this database with a total of 280 videos. Two kinds of cameras with different resolutions (720×480 and 640×480) were used to record the videos from the 35 individuals. For the real accesses, each individual has two video recordings captured with the Laptop cameras and Android, respectively. For the video attacks, two types of cameras, the iPhone and Canon cameras were used to capture high definition videos on each of the subject. The videos taken with Canon camera were then replayed on iPad Air screen to generate the HD replay attacks while the videos recorded by the iPhone mobile were replayed itself to generate the mobile replay attacks. Photo attacks were produced by printing the 35 subjects’ photos on A3 papers using HP colour printer. The recording videos with respect to the 35 individuals were divided into training (15 subjects with 120 videos) and testing (40 subjects with 160 videos) datasets, respectively."
},
{
"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": "ReCoRD",
"dval": "Reading Comprehension with Commonsense Reasoning Dataset (ReCoRD) is a large-scale reading comprehension dataset which requires commonsense reasoning. ReCoRD consists of queries automatically generated from CNN/Daily Mail news articles; the answer to each query is a text span from a summarizing passage of the corresponding news. The goal of ReCoRD is to evaluate a machine's ability of commonsense reasoning in reading comprehension. ReCoRD is pronounced as [ˈrɛkərd]."
},
{
"dkey": "MLSUM",
"dval": "A large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community."
}
] |
The proposed attention-based adversarial defense framework consists of a two-stage pipeline. The first stage is designed | adversarial defense images | 2,018 | [
"AnimalWeb",
"Raindrop",
"DramaQA",
"ECSSD",
"Fakeddit",
"WinoGrande",
"Spoken-SQuAD"
] | [
"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": "AnimalWeb",
"dval": "A large-scale, hierarchical annotated dataset of animal faces, featuring 21.9K faces from 334 diverse species and 21 animal orders across biological taxonomy. These faces are captured `in-the-wild' conditions and are consistently annotated with 9 landmarks on key facial features. The proposed dataset is structured and scalable by design; its development underwent four systematic stages involving rigorous, manual annotation effort of over 6K man-hours."
},
{
"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": "DramaQA",
"dval": "The DramaQA focuses on two perspectives: 1) Hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence. 2) Character-centered video annotations to model local coherence of the story. The dataset is built upon the TV drama \"Another Miss Oh\" and it contains 17,983 QA pairs from 23,928 various length video clips, with each QA pair belonging to one of four difficulty levels."
},
{
"dkey": "ECSSD",
"dval": "The Extended Complex Scene Saliency Dataset (ECSSD) is comprised of complex scenes, presenting textures and structures common to real-world images. ECSSD contains 1,000 intricate images and respective ground-truth saliency maps, created as an average of the labeling of five human participants."
},
{
"dkey": "Fakeddit",
"dval": "Fakeddit is a novel multimodal dataset for fake news detection consisting of over 1 million samples from multiple categories of fake news. After being processed through several stages of review, the samples are labeled according to 2-way, 3-way, and 6-way classification categories through distant supervision."
},
{
"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": "Spoken-SQuAD",
"dval": "In SpokenSQuAD, the document is in spoken form, the input question is in the form of text and the answer to each question is always a span in the document. The following procedures were used to generate spoken documents from the original SQuAD dataset. First, the Google text-to-speech system was used to generate the spoken version of the articles in SQuAD. Then CMU Sphinx was sued to generate the corresponding ASR transcriptions. The SQuAD training set was used to generate the training set of Spoken SQuAD, and SQuAD development set was used to generate the testing set for Spoken SQuAD. If the answer of a question did not exist in the ASR transcriptions of the associated article, the question-answer pair was removed from the dataset because these examples are too difficult for listening comprehension machine at this stage."
}
] |
I want to use a CNN-based tracking model. | visual tracking video | 2,019 | [
"SNIPS",
"LAG",
"AFLW2000-3D",
"DiCOVA",
"ConvAI2"
] | [
"OTB",
"VOT2017"
] | [
{
"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": "VOT2017",
"dval": "VOT2017 is a Visual Object Tracking dataset for different tasks that contains 60 short sequences annotated with 6 different attributes."
},
{
"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": "LAG",
"dval": "Includes 5,824 fundus images labeled with either positive glaucoma (2,392) or negative glaucoma (3,432)."
},
{
"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": "DiCOVA",
"dval": "The DiCOVA Challenge dataset is derived from the Coswara dataset, a crowd-sourced dataset of sound recordings from COVID-19 positive and non-COVID-19 individuals. The Coswara data is collected using a web-application2, launched in April-2020, accessible through the internet by anyone around the globe. The volunteering subjects are advised to record their respiratory sounds in a quiet environment. \n\nEach subject provides 9 audio recordings, namely, (a) shallow and deep breathing (2 nos.), (b) shallow and heavy cough (2 nos.), (c) sustained phonation of vowels [æ] (as in bat), [i] (as in beet), and [u] (as in boot) (3 nos.), and (d) fast and normal pace 1 to 20 number counting (2 nos.). \n\nThe DiCOVA Challenge has two tracks. The participants also provided metadata corresponding to their current health status (includes COVID19 status, any other respiratory ailments, and symptoms), demographic information, age and gender. From this Coswara dataset, two datasets have been created: \n\n(a) Track-1 dataset: composed of cough sound recordings. It t is composed of cough audio data from 1040 subjects.\n(b) Track-2 dataset: composed of deep breathing, vowel [i], and number counting (normal pace) speech recordings. It is composed of audio data from 1199 subjects."
},
{
"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."
}
] |
Instance mask projection is an end-to-end trainable operator that projects instance | semantic segmentation images top-view grid map sequences autonomous driving | 2,019 | [
"WikiReading",
"KnowledgeNet",
"THEODORE",
"PKU-MMD",
"LSHTC",
"SOBA",
"ISBDA"
] | [
"COCO",
"Cityscapes"
] | [
{
"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": "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": "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": "KnowledgeNet",
"dval": "KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).\n\nFor instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage: \"Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn, in Moravia, and lived about 1756...\""
},
{
"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": "PKU-MMD",
"dval": "The PKU-MMD dataset is a large skeleton-based action detection dataset. It contains 1076 long untrimmed video sequences performed by 66 subjects in three camera views. 51 action categories are annotated, resulting almost 20,000 action instances and 5.4 million frames in total. Similar to NTU RGB+D, there are also two recommended evaluate protocols, i.e. cross-subject and cross-view."
},
{
"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": "SOBA",
"dval": "A new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks."
},
{
"dkey": "ISBDA",
"dval": "Consists of user-generated aerial videos from social media with annotations of instance-level building damage masks. This provides the first benchmark for quantitative evaluation of models to assess building damage using aerial videos."
}
] |
I want to train a model to answer questions from text. | question answering text | 2,018 | [
"TextVQA",
"RecipeQA",
"CommonsenseQA",
"BREAK",
"TrecQA",
"Spoken-SQuAD"
] | [
"WebQuestions",
"SQuAD",
"TriviaQA"
] | [
{
"dkey": "WebQuestions",
"dval": "The WebQuestions dataset is a question answering dataset using Freebase as the knowledge base and contains 6,642 question-answer pairs. It was created by crawling questions through the Google Suggest API, and then obtaining answers using Amazon Mechanical Turk. The original split uses 3,778 examples for training and 2,032 for testing. All answers are defined as Freebase entities.\n\nExample questions (answers) in the dataset include “Where did Edgar Allan Poe died?” (baltimore) or “What degrees did Barack Obama get?” (bachelor_of_arts, juris_doctor)."
},
{
"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": "TriviaQA",
"dval": "TriviaQA is a realistic text-based question answering dataset which includes 950K question-answer pairs from 662K documents collected from Wikipedia and the web. This dataset is more challenging than standard QA benchmark datasets such as Stanford Question Answering Dataset (SQuAD), as the answers for a question may not be directly obtained by span prediction and the context is very long. TriviaQA dataset consists of both human-verified and machine-generated QA subsets."
},
{
"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": "RecipeQA",
"dval": "RecipeQA is a dataset for multimodal comprehension of cooking recipes. It consists of over 36K question-answer pairs automatically generated from approximately 20K unique recipes with step-by-step instructions and images. Each question in RecipeQA involves multiple modalities such as titles, descriptions or images, and working towards an answer requires (i) joint understanding of images and text, (ii) capturing the temporal flow of events, and (iii) making sense of procedural knowledge."
},
{
"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": "BREAK",
"dval": "Break is a question understanding dataset, aimed at training models to reason over complex questions. It features 83,978 natural language questions, annotated with a new meaning representation, Question Decomposition Meaning Representation (QDMR). Each example has the natural question along with its QDMR representation. Break contains human composed questions, sampled from 10 leading question-answering benchmarks over text, images and databases. This dataset was created by a team of NLP researchers at Tel Aviv University and Allen Institute for AI."
},
{
"dkey": "TrecQA",
"dval": "Text Retrieval Conference Question Answering (TrecQA) is a dataset created from the TREC-8 (1999) to TREC-13 (2004) Question Answering tracks. There are two versions of TrecQA: raw and clean. Both versions have the same training set but their development and test sets differ. The commonly used clean version of the dataset excludes questions in development and test sets with no answers or only positive/negative answers. The clean version has 1,229/65/68 questions and 53,417/1,117/1,442 question-answer pairs for the train/dev/test split."
},
{
"dkey": "Spoken-SQuAD",
"dval": "In SpokenSQuAD, the document is in spoken form, the input question is in the form of text and the answer to each question is always a span in the document. The following procedures were used to generate spoken documents from the original SQuAD dataset. First, the Google text-to-speech system was used to generate the spoken version of the articles in SQuAD. Then CMU Sphinx was sued to generate the corresponding ASR transcriptions. The SQuAD training set was used to generate the training set of Spoken SQuAD, and SQuAD development set was used to generate the testing set for Spoken SQuAD. If the answer of a question did not exist in the ASR transcriptions of the associated article, the question-answer pair was removed from the dataset because these examples are too difficult for listening comprehension machine at this stage."
}
] |
3D object recognition is an important component of many vision and robotics systems. | 3d object recognition voxels pixels | 2,016 | [
"OCID",
"3DNet",
"Flightmare Simulator",
"HoME"
] | [
"ImageNet",
"ModelNet"
] | [
{
"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": "ModelNet",
"dval": "The ModelNet40 dataset contains synthetic object point clouds. As the most widely used benchmark for point cloud analysis, ModelNet40 is popular because of its various categories, clean shapes, well-constructed dataset, etc. The original ModelNet40 consists of 12,311 CAD-generated meshes in 40 categories (such as airplane, car, plant, lamp), of which 9,843 are used for training while the rest 2,468 are reserved for testing. The corresponding point cloud data points are uniformly sampled from the mesh surfaces, and then further preprocessed by moving to the origin and scaling into a unit sphere."
},
{
"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."
},
{
"dkey": "3DNet",
"dval": "The 3DNet dataset is a free resource for object class recognition and 6DOF pose estimation from point cloud data. 3DNet provides a large-scale hierarchical CAD-model databases with increasing numbers of classes and difficulty with 10, 60 and 200 object classes together with evaluation datasets that contain thousands of scenes captured with an RGB-D sensor."
},
{
"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": "HoME",
"dval": "HoME (Household Multimodal Environment) is a multimodal environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more."
}
] |
I want to detect facial landmark and components simultaneously. | landmark-region-based facial detection images | 2,019 | [
"AFLW2000-3D",
"300-VW",
"LS3D-W",
"AFLW",
"AffectNet"
] | [
"Helen",
"AFW"
] | [
{
"dkey": "Helen",
"dval": "The HELEN dataset is composed of 2330 face images of 400×400 pixels with labeled facial components generated through manually-annotated contours along eyes, eyebrows, nose, lips and jawline."
},
{
"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": "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": "LS3D-W",
"dval": "A 3D facial landmark dataset of around 230,000 images."
},
{
"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."
}
] |
I would like to implement the Neural Architecture Search (NAS) approach and apply it | neural architecture search | 2,019 | [
"NAS-Bench-201",
"NAS-Bench-101",
"NATS-Bench",
"NAS-Bench-1Shot1",
"30MQA"
] | [
"ImageNet",
"Caltech-101",
"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": "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": "NAS-Bench-201",
"dval": "NAS-Bench-201 is a benchmark (and search space) for neural architecture search. Each architecture consists of a predefined skeleton with a stack of the searched cell. In this way, architecture search is transformed into the problem of searching a good cell."
},
{
"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": "NATS-Bench",
"dval": "A unified benchmark on searching for both topology and size, for (almost) any up-to-date NAS algorithm. NATS-Bench includes the search space of 15,625 neural cell candidates for architecture topology and 32,768 for architecture size on three datasets."
},
{
"dkey": "NAS-Bench-1Shot1",
"dval": "NAS-Bench-1Shot1 draws on the recent large-scale tabular benchmark NAS-Bench-101 for cheap anytime evaluations of one-shot NAS methods."
},
{
"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."
}
] |
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Task: MCQ with multiple correct answers.
Dataset: Recommendation of datasets to validate a research question.
This dataset is derived from the DataFinder dataset. We curate the abstracts of each dataset from PapersWithCode.
Given is a short query discussing a research question, and keyphrases relevant the query.
The original training set of the DataFinder dataset has positive and negative candidates for each query, to train a contrastive model.
We objective is to convert the dataset into a MCQ question-answering task with multiple correct answers. We also add the abstracts from the research papers introducing the datasets so that context can be provided to the models.
To reproduce the construction of this dataset, please visit https://github.com/shruti-singh/scidata_recommendation.
Please note that the query instances in this dataset have no intersection with the dataset_recommendation_mcq_sc dataset. dataset_recommendation_mcq_sc is a variant of this MCQ question-answering task with only single correct answer.
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