Omkar2118/datasets huggingface dataset:
WSVD
Full name: Web Stereo Video Dataset

The Web Stereo Video Dataset consists of 553 stereoscopic videos from YouTube. This dataset has a wide variety of scene types, and features many nonrigid objects.

Introducing paper: Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes
XED

XED is a multilingual fine-grained emotion dataset. The dataset consists of human-annotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing new resources for many low-resource languages. 

Source: [XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection](/paper/xed-a-multilingual-dataset-for-sentiment)

Introducing paper: XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection
XKCDColors

A balanced dataset of color names and RGB values for training classifiers.

Source: [https://github.com/Smoltbob/XKCDColors-Dataset](https://github.com/Smoltbob/XKCDColors-Dataset)

Introducing paper: Color inference from semantic labeling for person search in videos
XL-R2R
Full name: Cross-lingual Room-to-Room

The **XL-R2R** dataset is built upon the R2R dataset and extends it with Chinese instructions. XL-R2R preserves the same splits as in R2R and thus consists of train, val-seen, and val-unseen splits with both English and Chinese instructions, and test split with English instructions only.

Source: [https://github.com/zzxslp/Crosslingual-VLN](https://github.com/zzxslp/Crosslingual-VLN)

Introducing paper: Cross-Lingual Vision-Language Navigation
XL-WiC

A large multilingual benchmark, XL-WiC, featuring gold standards in 12 new languages from varied language families and with different degrees of resource availability, opening room for evaluation scenarios such as zero-shot cross-lingual transfer.

Source: [XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization](/paper/xl-wic-a-multilingual-benchmark-for)

Introducing paper: XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization
X-MARS

The **X-MARS** dataset proposes new splits for the MARS dataset, to allow for cross-evaluation with the Market-1501 dataset without training and test overlap between the two datasets.

Source: [https://github.com/andreas-eberle/x-mars](https://github.com/andreas-eberle/x-mars)

Introducing paper: Pose-Driven Deep Models for Person Re-Identification
XOR-TYDI QA

A large-scale dataset built on questions from TyDi QA lacking same-language answers.

Source: [XOR QA: Cross-lingual Open-Retrieval Question Answering](/paper/xor-qa-cross-lingual-open-retrieval-question)

Introducing paper: XOR QA: Cross-lingual Open-Retrieval Question Answering
LAReQA

A challenging new benchmark for language-agnostic answer retrieval from a multilingual candidate pool. 

Source: [LAReQA: Language-agnostic answer retrieval from a multilingual pool](/paper/lareqa-language-agnostic-answer-retrieval)

Introducing paper: LAReQA: Language-agnostic answer retrieval from a multilingual pool
X-ray and Visible Spectra Circular Motion Images Dataset

Collections of images of the same rotating plastic object made in X-ray and visible spectra. Both parts of the dataset contain 400 images. The images are maid every 0.5 degrees of the object axial rotation. The collection of images is designed for evaluation of the performance of circular motion estimation algorithms as well as for the study of X-ray nature influence on the image analysis algorithms such as keypoints detection and description.

Source: [X-ray and Visible Spectra Circular Motion Images Dataset](/paper/x-ray-and-visible-spectra-circular-motion)

Introducing paper: X-ray and Visible Spectra Circular Motion Images Dataset
xR-EgoPose

xR-EgoPose is an egocentric synthetic dataset for egocentric 3D human pose estimation. It consists of ~380 thousand photo-realistic egocentric camera images in a variety of indoor and outdoor spaces.

Introducing paper: xR-EgoPose: Egocentric 3D Human Pose from an HMD Camera
X-SRL

SRL is the task of extracting semantic predicate-argument structures from sentences. **X-SRL** is a multilingual parallel Semantic Role Labelling (SRL) corpus for English (EN), German (DE), French (FR) and Spanish (ES) that is based on English gold annotations and shares the same labelling scheme across languages.

Source: [https://github.com/Heidelberg-NLP/xsrl_mbert_aligner](https://github.com/Heidelberg-NLP/xsrl_mbert_aligner)
Image Source: [Daza et al](https://arxiv.org/pdf/2010.01998v1.pdf)

Introducing paper: X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset
x-stance

A large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets).

Source: [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](/paper/x-stance-a-multilingual-multi-target-dataset)

Introducing paper: X-Stance: A Multilingual Multi-Target Dataset for Stance Detection
YASO

YASO is a crowd-sourced TSA evaluation dataset, collected using a new annotation scheme for labeling targets and their sentiments. The dataset contains 2,215 English sentences from movie, business and product reviews, and 7,415 terms and their corresponding sentiments annotated within these sentences. 

Source: [YASO: A New Benchmark for Targeted Sentiment Analysis](/paper/yaso-a-new-benchmark-for-targeted-sentiment)

Introducing paper: YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews
YCBInEOAT Dataset

A new dataset with significant occlusions related to object manipulation.

Source: [se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains](/paper/se-3-tracknet-data-driven-6d-pose-tracking-by)

Introducing paper: se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
YFCC100M

YFCC100M is a that dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. Each media object in the dataset is represented by several pieces of metadata, e.g. Flickr identifier, owner name, camera, title, tags, geo, media source. The collection provides a comprehensive snapshot of how photos and videos were taken, described, and shared over the years, from the inception of Flickr in 2004 until early 2014.

Source: [YFCC100M: The New Data in Multimedia Research](https://arxiv.org/pdf/1503.01817v2.pdf)
Image Source: [Thomee et al](https://arxiv.org/pdf/1503.01817v2.pdf)

Introducing paper: YFCC100M: The New Data in Multimedia Research
Yoga-82

Dataset for large-scale yoga pose recognition with 82 classes.

Source: [Yoga-82: A New Dataset for Fine-grained Classification of Human Poses](/paper/yoga-82-a-new-dataset-for-fine-grained)

Introducing paper: Yoga-82: A New Dataset for Fine-grained Classification of Human Poses
Youtubean

Youtbean is a dataset created from closed captions of YouTube product review videos. It can be used for aspect extraction and sentiment classification.

Source: [https://arxiv.org/pdf/1708.02420.pdf](https://arxiv.org/pdf/1708.02420.pdf)

Introducing paper: Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN
YT-BB
Full name: YouTube-BoundingBoxes

YouTube-BoundingBoxes (YT-BB) is a large-scale data set of video URLs with densely-sampled object bounding box annotations. The data set consists of approximately 380,000 video segments about 19s long, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone camera. The objects represent a subset of the MS COCO label set. All video segments were human-annotated with high-precision classification labels and bounding boxes at 1 frame per second. 

Source: [YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video](https://arxiv.org/pdf/1702.00824.pdf)

Introducing paper: YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
YouTube Movie Summaries

This dataset contains 94 movie summary videos from various YouTube channels.

Source: [YouTube Movie Summaries](https://github.com/pelindogan/NeuMATCH)

Introducing paper: A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)
YUP++
Full name: YUP++ Dynamic Scenes dataset

A new and challenging video database of dynamic scenes that more than doubles the size of those previously available. This dataset is explicitly split into two subsets of equal size that contain videos with and without camera motion to allow for systematic study of how this variable interacts with the defining dynamics of the scene per se. 

Source: [Temporal Residual Networks for Dynamic Scene Recognition](/paper/temporal-residual-networks-for-dynamic-scene)

Introducing paper: Temporal Residual Networks for Dynamic Scene Recognition
ZEST

A new English language dataset structured for task-oriented evaluation on unseen tasks. 

Source: [Learning from Task Descriptions](/paper/learning-from-task-descriptions)

Introducing paper: Learning from Task Descriptions
Zooniverse
Full name: HumBug Zooniverse

The Humbug Zooinverse dataset is a dataset of mosquito audio recordings. With over a thousand contributors, it contains 195,434 labels of two second duration, of which approximately 10 percent signify mosquito events.

Source: [https://github.com/HumBug-Mosquito/ZooniverseData](https://github.com/HumBug-Mosquito/ZooniverseData)

Introducing paper: HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset
ZuBuD+

A more balanced version of ZuBuD.

Source: [A location-aware embedding technique for accurate landmark recognition](/paper/a-location-aware-embedding-technique-for)

Introducing paper: A location-aware embedding technique for accurate landmark recognition
Spot-the-diff

Spot-the-diff is a dataset consisting of 13,192 image pairs along with corresponding human provided text annotations stating the differences between the two images.

Source: [Learning to Describe Differences Between Pairs of Similar Images](https://arxiv.org/pdf/1808.10584.pdf)

Introducing paper: Learning to Describe Differences Between Pairs of Similar Images
OST300
Full name: OST300

**OST300** is an outdoor scene dataset with 300 test images of outdoor scenes, and a training set of 7 categories of images with rich textures.

Source: [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://paperswithcode.com/paper/recovering-realistic-texture-in-image-super)
Image Source: [http://mmlab.ie.cuhk.edu.hk/projects/SFTGAN/](http://mmlab.ie.cuhk.edu.hk/projects/SFTGAN/)

Introducing paper: Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
CoNSeP
Full name: Colorectal Nuclear Segmentation and Phenotypes

The colorectal nuclear segmentation and phenotypes (CoNSeP) dataset consists of 41 H&E stained image tiles, each of size 1,000×1,000 pixels at 40× objective magnification. The images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs, each belonging to an individual patient, and scanned with an Omnyx VL120 scanner within the department of pathology at University Hospitals Coventry and Warwickshire, UK.

Source: [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://arxiv.org/pdf/1812.06499.pdf)
Image Source: [Graham et al](https://arxiv.org/pdf/1812.06499.pdf)

Introducing paper: HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
CUHK Image Cropping
Full name: CUHK Image Cropping

**CUHK Image Cropping**  is a dataset for image cropping. The photos are of varying aesthetic quality and span a variety of image categories, including animal, architecture, human, landscape, night, plant and man-made objects. Each image is manually cropped by three expert photographers (graduate students in art whose primary medium is photography) to form three training sets. There are 1,000 photos in the dataset.

Introducing paper: Learning the Change for Automatic Image Cropping
MIT Traffic
Full name: MIT Traffic

**MIT Traffic** is a dataset for research on activity analysis and crowded scenes. It includes a traffic video sequence of 90 minutes long. It is recorded by a stationary camera. The size of the scene is 720 by 480 and it is divided into 20 clips.

Source: [MIT Traffic Dataset](http://mmlab.ie.cuhk.edu.hk/datasets/mit_traffic/index.html)

Introducing paper: Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models
PCN
Full name: Pedestrian Color Naming

**Pedestrian Color Naming (PCN)** is a dataset for pedestrian color naming, which contains 14,213 images, each of which hand-labeled with color label for each pixel. All images in the PCN dataset are obtained from the Market- 1501 dataset.
Social Relation Dataset
Full name: Social Relation Dataset

**Social Relation Dataset** is a dataset for social relation trait prediction from face images. Traits are based on the interpersonal circle proposed by Kiesler, where human relations are divided into 16 segments. Each segment has its opposite side in the circle, such as 'friendly and hostile'. The dataset contains 8,306 images chosen from the internet and movies. Each image is labelled with faces’ bounding boxes and their pairwise relations. 

Source: [Open MMLab](https://openmmlab.com/data.html)

Introducing paper: Learning Social Relation Traits from Face Images
WWW Crowd
Full name: WWW Crowd

**WWW Crowd** provides 10,000 videos with over 8 million frames from 8,257 diverse scenes, therefore offering a comprehensive dataset for the area of crowd understanding.

Introducing paper: Deeply Learned Attributes for Crowded Scene Understanding
PolyU

PolyU Dataset is a large dataset of real-world noisy images with reasonably obtained corresponding “ground truth” images. The basic idea is to capture the same and unchanged scene for many (e.g., 500) times and compute their mean image, which can be roughly taken as the “ground truth” image for the real-world noisy images. The rational of this strategy is that for each pixel, the noise is generated randomly larger or smaller than 0. Sampling the same pixel many times and computing the average value will approximate the truth pixel value and alleviate significantly the noise.

Source: [Real-world Noisy Image Denoising: A New Benchmark](https://arxiv.org/pdf/1804.02603.pdf)

Introducing paper: Real-world Noisy Image Denoising: A New Benchmark
SPOT
Full name: Sentiment Polarity Annotations Dataset

The SPOT dataset contains 197 reviews originating from the Yelp'13 and IMDB collections ([1][2]), annotated with segment-level polarity labels (positive/neutral/negative). Annotations have been gathered on 2 levels of granulatiry:

- Sentences
- Elementary Discourse Units (EDUs), i.e. sub-sentence clauses produced by a state-of-the-art RST parser

This dataset is intended to aid sentiment analysis research and, in particular, the evaluation of methods that attempt to predict sentiment on a fine-grained, segment-level basis.

Source: [SPOT](https://github.com/EdinburghNLP/spot-data)

Introducing paper: Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
ToM QA

The data consists of a set of 3 task types and 4 question types, creating 12 total scenarios. The tasks are grouped into stories, which are denoted by the numbering at the start of each line.

Source: [ToM QA](https://github.com/kayburns/tom-qa-dataset)

Introducing paper: Evaluating Theory of Mind in Question Answering
Verse

Verse is a new dataset that augments existing multimodal datasets (COCO and TUHOI) with sense labels. 

Source: [Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings](https://www.aclweb.org/anthology/N16-1022.pdf)

Introducing paper: Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
Holl-E

Holl-E is a dataset containing movie chats wherein each response is explicitly generated by copying and/or modifying sentences from unstructured background knowledge such as plots, comments and reviews about the movie.

Source: [Towards Exploiting Background Knowledge for Building Conversation Systems](https://arxiv.org/pdf/1809.08205.pdf)

Introducing paper: Towards Exploiting Background Knowledge for Building Conversation Systems
Wikidata-Disamb

The Wikidata-Disamb dataset is intended to allow a clean and scalable evaluation of NED with Wikidata entries, and to be used as a reference in future research.

Source: [Named Entity Disambiguation using Deep Learning on Graphs](https://arxiv.org/pdf/1810.09164.pdf)

Introducing paper: Named Entity Disambiguation using Deep Learning on Graphs
Gutenberg Poem Dataset

Gutenberg Poem Dataset is used for the next verse prediction component.

Source: [Gutenberg Poem Dataset](https://arxiv.org/pdf/2011.02686.pdf)

Introducing paper: Investigating Societal Biases in a Poetry Composition System
Visual Beliefs

Visual Beliefs is a dataset of abstract scenes to study visual beliefs. The dataset consists of 8-frame scenes, and in each scene a person has a mistaken belief. The dataset can be used for two tasks: predicting who is mistaken and predicting when are they mistaken. 

Source: [Who is Mistaken?](https://arxiv.org/pdf/1612.01175.pdf)

Introducing paper: Who is Mistaken?
MovieShots

MovieShots is a dataset to facilitate the shot type analysis in videos. It is a large-scale shot type annotation set that contains 46K shots from 7,858 movies covering a wide
variety of movie genres to ensure the inclusion of all scale and movement types of shot. Each shot has two attributes, shot scale and shot movement.

Shot scale has five categories: 1) long shot (LS) is taken from a long distance, sometimes as far as a quarter of a mile away; 2) full shot (FS) barely includes the human body in full; 3) medium shot (MS) contains a figure from the knees or waist up; 4) close-up shot (CS) concentrates on a relatively small object, showing the face of the hand of a person; (5) extreme close-up shot (ECS) shows even smaller parts such as the image of an eye or a mouth.

Shot movement has four categories: 1) in static shot, the camera is fixed but the subject is flexible to move; 2) for motion shot, the camera moves or rotates; 3) the camera zooms in for push shot, and 4) zooms out for pull shot. While all the four movement types are widely used in movies, the use of push and pull shots only takes a very small portion. The usage of different shots usually depends on the movie genres and the preferences of the filmmakers.

Source: [A Unified Framework for Shot Type Classification Based on Subject Centric Lens](https://anyirao.com/projects/ShotType.html)

Introducing paper: A Unified Framework for Shot Type Classification Based on Subject Centric Lens
RDD-2020
Full name: Road Damage Dataset 2020

The Road Damage Dataset 2020 (RDD-2020) Secondly is a large-scale heterogeneous dataset comprising 26620 images collected from multiple countries using smartphones. The images are collected from roads in India, Japan and the Czech Republic.

Source: [Transfer Learning-based Road Damage Detection for Multiple Countries](https://arxiv.org/abs/2008.13101)

Introducing paper: Transfer Learning-based Road Damage Detection for Multiple Countries
Sarcasm Corpus V2

The Sarcasm Corpus contains sarcastic and non-sarcastic utterances of three different types, which are balanced with half of the samples being sarcastic and half non-sarcastic.
The three types are:

* Generic: 6,520 samples
* Rhetorical Questions: 1,702 samples
* Hyperbole: 1,164 samples

Source: [Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue](https://arxiv.org/pdf/1709.05404.pdf)

Introducing paper: Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue
3D-FUTURE

3D-FUTURE (3D FUrniture shape with TextURE) is a 3D dataset that contains 20,240 photo-realistic synthetic images captured in 5,000 diverse scenes, and 9,992 involved unique industrial 3D CAD shapes of furniture with high-resolution informative textures developed by professional designers.

Source: [3D-FUTURE](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-future)
Image Source: [https://tianchi.aliyun.com/specials/promotion/alibaba-3d-future](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-future)

Introducing paper: 3D-FUTURE: 3D Furniture shape with TextURE
CC-Stories
Full name: CC-Stories

**CC-Stories** (or STORIES) is a dataset for common sense reasoning and language modeling. It was constructed by aggregating documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions in commonsense reasoning tasks. The top 1.0% of highest ranked documents is chosen as the new training corpus.

Introducing paper: A Simple Method for Commonsense Reasoning
BrixIA
Full name: BrixIA Covid-19

**BrixIA Covid-19** is a large dataset of CXR images corresponding to the entire amount of images taken for both triage and patient monitoring in sub-intensive and intensive care units during one month (between March 4th and April 4th 2020) of pandemic peak at the ASST Spedali Civili di Brescia, and contains all the variability originating from a real clinical scenario. It includes 4,707 CXR images of COVID-19 subjects, acquired with both CR and DX modalities, in AP or PA projection, and retrieved from the facility RIS-PACS system.

Introducing paper: BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset
MaleX
Full name: MaleX

**MaleX** is a curated dataset of malware and benign Windows executable samples for malware researchers. The dataset contains 1,044,394 Windows executable binaries with 864,669 labelled as malware and 179,725 as benign. This dataset has reasonable number of samples and is sufficient to test data-driven machine learning classification methods and also to measure the performance of the designed models in terms of scalability and adaptability.

Introducing paper: Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning
AIST++

**AIST++** is a 3D dance dataset which contains 3D motion reconstructed from real dancers paired with music. The AIST++ Dance Motion Dataset is constructed from the AIST Dance Video DB. With multi-view videos, an elaborate pipeline is designed to estimate the camera parameters, 3D human keypoints and 3D human dance motion sequences:

- It provides 3D human keypoint annotations and camera parameters for 10.1M images, covering 30 different subjects in 9 views. These attributes makes it the largest and richest existing dataset with 3D human keypoint annotations.
- It also contains 1,408 sequences of 3D human dance motion, represented as joint rotations along with root trajectories. The dance motions are equally distributed among 10 dance genres with hundreds of choreographies. Motion durations vary from 7.4 sec. to 48.0 sec. All the dance motions have corresponding music.
ScenicOrNot

ScenicOrNot (SoN) is a dataset of 185,548 images with associated natural beauty rating histograms. Each image in the dataset was rated at least five times. The images also have metadata like title and location.

Source: [Understanding and Mapping Natural Beauty](https://arxiv.org/abs/1612.03142)

Introducing paper: Understanding and Mapping Natural Beauty
TiMoS
Full name: Tropes in Movie Synopses

**Tropes in Movie Synopses (TiMoS)** is a dataset of movie tropes collected from a Wikipedia-style website, TVTropes3 with 5623 movie synopses associated with 95 most occurred tropes. The movies are diverse in genre, filming year, length, and style, making the task challenging and unable to rely on patterns from a specific domain. The tropes involve character trait, role interaction, situation, and storyline, which could be sensed by a non-expert human but remains challenging for machines that have more than 100 million parameters and pre-trained with 11,000 books and the whole
Wikipedia (23.97 F1 score while a human could reach 64.87).
Kite

The Kite database is a multi-modal dataset for the control of unmanned aerial vehicles (UAVs). There are three modalities present in the dataset:

- Language, represented by the commands issued to the UAV
- Audio, represented by the spoken instantiation of the commands
- Visual, represented by an image that is likely to be seen when the command is issued

The dataset was created by the members of the [SpeeD](https://speed.pub.ro/) team.

Source: [Kite Dataset](http://kite.speed.pub.ro/)

Introducing paper: Kite: Automatic speech recognition for unmanned aerial vehicles
COCO Earthquake

**COCO Earthquake** is a dataset similar to Common Objects in Context (COCO) used for cracking segmentation. The images selected in the dataset are at various scales, and the tool referred to as the COCO Annotator is used to label cracks for training. In these labeled images, cracks are in yellow and background is in purple. Size of the training and labeling images is varied from 168×300 to 4600×3070. By excluding steel structures, 2,021 images are labeled when surface cracks appeared on structural or nonstructural materials at various scales.

Introducing paper: End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
VLUC
Full name: Video-Like Urban Computing

VLUC (Video-Like Urban Computing) is a benchmark for video-like computing on citywide traffic density and crowd prediction. It consists of two new datasets BousaiTYO and BousaiOSA and existing datasets TaxiBJ, BikeNYC I-II, and TaxiNYC.


Source: [VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction](https://arxiv.org/abs/1911.06982)

Introducing paper: VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction
MM-WHS 2017
Full name: MM-WHS 2017

The **MM-WHS 2017** dataset is a dataset for multi-modality whole heart segmentation. It provides 20 labeled and 40 unlabeled CT volumes, as well as 20 labeled and 40 unlabeled MR volumes. In total there are 120 multi-modality cardiac images acquired in a real clinical environment.
BosphorusSign22k

BosphorusSign22k is a benchmark dataset for vision-based user-independent isolated Sign Language Recognition (SLR). The dataset is based on the BosphorusSign (Camgoz et al., 2016c) corpus which was collected with the purpose of helping both linguistic and computer science communities. It contains isolated videos of Turkish Sign Language glosses from three different domains: Health, finance and commonly used everyday signs. Videos in this dataset were performed by six native signers, which makes this dataset valuable for user independent sign language studies.

Source: [BosphorusSign22k Sign Language Recognition Dataset](https://arxiv.org/abs/2004.01283)

Introducing paper: BosphorusSign22k Sign Language Recognition Dataset
EMIDEC
Full name: MICCAI 2020 EMIDEC

The **MICCAI 2020 EMIDEC** dataset is a dataset for classifying normal and pathological cases from the clinical information with or without DE-MRI, and secondly to automatically detect the different relevant areas (the myocardial contours, the infarcted area and the permanent microvascular obstruction area (no-reflow area)) from a series of short-axis DE-MRI covering the left ventricle. The segmentation allows one to make a quantification of the MI, in absolute value (mm3) or percentage of the myocardium.

The database consists of 150 exams (all from different patients) divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are provided to distinguish normal and pathological cases. The training set has 100 cases.

Lalande, A.; Chen, Z.; Decourselle, T.; Qayyum, A.; Pommier, T.; Lorgis, L.; de la Rosa, E.; Cochet, A.; Cottin, Y.; Ginhac, D.; Salomon, M.; Couturier, R.; Meriaudeau, F. Emidec: A Database Usable for the Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI. Data 2020, 5, 89.
doi: https://doi.org/10.3390/data5040089
InstaCities1M

InstaCities1M is a dataset of social media images with associated text. It consists of Instagram images associated associated with one of the 10 most populated English speaking cities all over the world. It has 100K images for each city, which makes a total of 1M images, split in 800K training images, 50K validation images and 150K testing images. All images were resized to 300x300 pixels.

Source: [The InstaCities1M Dataset](https://gombru.github.io/2018/08/01/InstaCities1M/)

Introducing paper: Learning to Learn from Web Data through Deep Semantic Embeddings
WHU-Hi
Full name: Wuhan UAV-borne hyperspectral image

WHU-Hi dataset (Wuhan UAV-borne hyperspectral image) is collected and shared by the RSIDEA research group of Wuhan University, and it could serve as a benchmark dataset for precise crop classification and hyperspectral image classification studies. The WHU-Hi dataset contains three individual UAV-borne hyperspectral datasets: WHU-Hi-LongKou, WHU-Hi-HanChuan, and WHU-Hi-HongHu. All the datasets were acquired in farming areas with various crop types in Hubei province, China, via a Headwall Nano-Hyperspec sensor mounted on a UAV platform. Compared with spaceborne and airborne hyperspectral platforms, unmanned aerial vehicle (UAV)-borne hyperspectral systems can acquire hyperspectral imagery with a high spatial resolution (which we refer to here as H2 imagery). The research was published in Remote Sensing of Environment.

Source: [http://rsidea.whu.edu.cn/resource_WHUHi_sharing.htm](http://rsidea.whu.edu.cn/resource_WHUHi_sharing.htm)
Image Source: [http://rsidea.whu.edu.cn/resource_WHUHi_sharing.htm](http://rsidea.whu.edu.cn/resource_WHUHi_sharing.htm)

Introducing paper: WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification
Botswana
Full name: Botswana

**Botswana** is a hyperspectral image classification dataset. The NASA EO-1 satellite acquired a sequence of data over the Okavango Delta, Botswana in 2001-2004. The Hyperion sensor on EO-1 acquires data at 30 m pixel resolution over a 7.7 km strip in 242 bands covering the 400-2500 nm portion of the spectrum in 10 nm windows. Preprocessing of the data was performed by the UT Center for Space Research to mitigate the effects of bad detectors, inter-detector miscalibration, and intermittent anomalies. Uncalibrated and noisy bands that cover water absorption features were removed, and the remaining 145 bands were included as candidate features: [10-55, 82-97, 102-119, 134-164, 187-220]. The data analyzed in this study, acquired May 31, 2001, consist of observations from 14 identified classes representing the land cover types in seasonal swamps, occasional swamps, and drier woodlands located in the distal portion of the Delta.

Source: M Graña, MA Veganzons, B Ayerdi
Houston
Full name: Houston

**Houston** is a hyperspectral image classification dataset. The hyperspectral imagery consists of 144 spectral bands in the 380 nm to 1050 nm region and has been calibrated to at-sensor spectral radiance units, SRU =$ \mu \text{W} /( \text{cm}^2 \text{ sr nm})$. The corresponding co-registered DSM consists of elevation in meters above sea level (per the Geoid 2012A model).
Pavia Centre
Full name: Pavia Centre

**Pavia Centre** is a hyperspectral dataset acquired by the ROSIS sensor during a flight campaign over Pavia, northern Italy. The number of spectral bands is 102 for Pavia Centre. Pavia Centre is a 1096*1096 pixels image. The geometric resolution is 1.3 meters. Image groundtruths differentiate 9 classes each. Pavia scenes were provided by Prof. Paolo Gamba from the Telecommunications and Remote Sensing Laboratory, Pavia university (Italy).
SEVIR
Full name: Storm EVent ImagRy

**SEVIR** is an annotated, curated and spatio-temporally aligned dataset containing over 10,000 weather events that each consist of 384 km x 384 km image sequences spanning 4 hours of time. Images in SEVIR were sampled and aligned across five different data types: three channels (C02, C09, C13) from the GOES-16 advanced baseline imager, NEXRAD vertically integrated liquid mosaics, and GOES-16 Geostationary Lightning Mapper (GLM) flashes. Many events in SEVIR were selected and matched to the NOAA Storm Events database so that additional descriptive information such as storm impacts and storm descriptions can be linked to the rich imagery provided by the sensors.

Source: [https://proceedings.neurips.cc//paper/2020/file/fa78a16157fed00d7a80515818432169-Paper.pdf](https://proceedings.neurips.cc//paper/2020/file/fa78a16157fed00d7a80515818432169-Paper.pdf)

Introducing paper: SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology
Chinese Classifier

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.
The 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.

Source: [https://github.com/wuningxi/ChineseClassifierDataset](https://github.com/wuningxi/ChineseClassifierDataset)

Introducing paper: ClassifierGuesser: A Context-based Classifier Prediction System for Chinese Language Learners
SimpleDBpediaQA

A new benchmark dataset for simple question answering over knowledge graphs that was created by mapping SimpleQuestions entities and predicates from Freebase to DBpedia. 

Source: [Farewell Freebase: Migrating the SimpleQuestions Dataset to DBpedia](/paper/farewell-freebase-migrating-the)

Introducing paper: Farewell Freebase: Migrating the SimpleQuestions Dataset to DBpedia
Part Whole Relations

The Part-Whole Relations dataset is a dataset of semantic relations between entities. It contains the following subtypes:
- Component-Of
- Member-Of
- Portion-Of
- Stuff-Of
- Located-In
- Contained-In
- Phase-Of
- Participates-In

Source: [https://github.com/pvthuy/part-whole-relations](https://github.com/pvthuy/part-whole-relations)

Introducing paper: Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction
ISI-PPT

This is a Dataset for Arabic/English text detection and optical character recognition. All image data are text-slides extracted from PowerPoint files downloaded from Internet through the Google API.
All annotations are automatically generated mainly through the WinCom32 Python API. Postprocess is also applied to place a more accurate text bounding box or to suppress false-alarms, e.g. a text box only containing spaces. Finally, all annotation results are briefly reviewed by human to reject extreme bad samples, e.g. a slide with a large portion of copied table as image. In summary, this dataset contains 10,692 images, and roughly 100K line samples.

Source: [https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset](https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset)
Image Source: [https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset](https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset)
MeQSum

**MeQSum** is a dataset for medical question summarization. It contains 1,000 summarized consumer health questions.

Source: [https://www.aclweb.org/anthology/P19-1215.pdf](https://www.aclweb.org/anthology/P19-1215.pdf)
Image Source: [https://www.aclweb.org/anthology/P19-1215.pdf](https://www.aclweb.org/anthology/P19-1215.pdf)

Introducing paper: On the Summarization of Consumer Health Questions
Visual Choice of Plausible Alternatives
Full name: VCOPA

Visual Choice of Plausible Alternatives (VCOPA) is an evaluation dataset containing 380 VCOPA questions and over 1K images with various topics, which is amenable to automatic evaluation, and present the performance of baseline reasoning approaches as initial benchmarks for future systems.

Source: [Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning](/paper/visual-choice-of-plausible-alternatives-an)

Introducing paper: Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning
QTuna

The QTUNA dataset is the result of a series of elicitation experiments in which human speakers were asked to perform a linguistic task that invites the use of quantified expressions in order to inform possible Natural Language Generation algorithms that mimic humans' use of quantified expressions.

Source: [https://github.com/a-quei/qtuna](https://github.com/a-quei/qtuna)

Introducing paper: QTUNA: A Corpus for Understanding How Speakers Use Quantification
LDDRS
Full name: LWIR DoFP Dataset of Road Scene

The **LWIR DoFP Dataset of Road Scene** (**LDDRS**) is a road detection dataset with 2,113 annotated images. It contains both day and night scenes, with multiple cars and pedestrians per image.

Source: [https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700460.pdf](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700460.pdf)

Introducing paper: Full-Time Monocular Road Detection Using Zero-Distribution Prior of Angle of Polarization
VegFru

**VegFru** is a domain-specific dataset for fine-grained visual categorization. VegFru categorizes vegetables and fruits according to their eating characteristics, and each image contains at least one edible part of vegetables or fruits with the same cooking usage. Particularly, all the images are labelled hierarchically. The current version covers vegetables and fruits of 25 upper-level categories and 292 subordinate classes. And it contains more than 160,000 images in total and at least 200 images for each subordinate class.

Source: [https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf](https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf)
Image Source: [https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf](https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf)

Introducing paper: VegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization
Para-Quality

Used to investigate common crowdsourced paraphrasing issues and for detecting the quality issues.

Source: [A Study of Incorrect Paraphrases in Crowdsourced User Utterances](/paper/a-study-of-incorrect-paraphrases-in)

Introducing paper: A Study of Incorrect Paraphrases in Crowdsourced User Utterances
Metaphorical Connections

The **Metaphorical Connections** dataset is a poetry dataset that contains annotations between metaphorical prompts and short poems. Each poem is annotated whether or not it successfully communicates the idea of the metaphorical prompt.

Source: [https://github.com/kgero/metaphorical-connections](https://github.com/kgero/metaphorical-connections)

Introducing paper: Challenges in Finding Metaphorical Connections
German affixoid dataset
Full name: GerAff

German affixoids are a type of morpheme in between affixes and free stems.

Source: [Distinguishing affixoid formations from compounds](https://www.aclweb.org/anthology/C18-1325.pdf)
Cross-Modal Comments Dataset

Cross Modal Automatic Commenting (CMAC) is a task which aims to automatically generate comments for graphic news. The CMAC dataset is a large-scale dataset for this task which consists of 24,134 graphic news. Each instance is composed of several news photos, news title, news body, and corresponding high-quality comments.

Source: [https://github.com/lancopku/CMAC](https://github.com/lancopku/CMAC)

Introducing paper: Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information
STREETS

A novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL.

Source: [STREETS: A Novel Camera Network Dataset for Traffic Flow](/paper/streets-a-novel-camera-network-dataset-for)

Introducing paper: STREETS: A Novel Camera Network Dataset for Traffic Flow
PubMed PICO Element Detection Dataset

PICO is a framework to formulate a well-defined focused clinical question. This framework identifies the sentences in a given medical text that belong to the four components: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). The PubMed PICO Element Detection dataset is a dataset for evaluating models that automatically detect PICO elements.

Source: [https://github.com/jind11/PubMed-PICO-Detection](https://github.com/jind11/PubMed-PICO-Detection)

Introducing paper: PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks
SpatialVOC2K

A multilingual image dataset with spatial relation annotations and object features for image-to-text generation, built using 2,026 images from the PASCAL VOC2008 dataset. 

Source: [SpatialVOC2K: A Multilingual Dataset of Images with Annotations and Features for Spatial Relations between Objects](/paper/spatialvoc2k-a-multilingual-dataset-of-images)

Introducing paper: SpatialVOC2K: A Multilingual Dataset of Images with Annotations and Features for Spatial Relations between Objects
CC-DBP

**CC-DBP** is a dataset for knowledge base population research using Common Crawl and DBpedia.

Source: [https://github.com/IBM/cc-dbp](https://github.com/IBM/cc-dbp)

Introducing paper: A Dataset for Web-Scale Knowledge Base Population
Earnings Call

The Earning Calls dataset consists of processed earning conference calls data (text and audio). It can be used to predict financial risk from both textual and vocal features from conference calls.

Source: [https://www.aclweb.org/anthology/P19-1038/](https://www.aclweb.org/anthology/P19-1038/)

Introducing paper: What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues
Processed Twitter

Processed Twitter is a dataset that is used for Twitter topic recognition.  It contains tweets from 6 different topics.

Source: [https://arxiv.org/pdf/1908.09931.pdf](https://arxiv.org/pdf/1908.09931.pdf)

Introducing paper: Multi-stage Deep Classifier Cascades for Open World Recognition
Grasp MultiObject

Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data.
This dataset is annotated using the same protocol as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.

Source: [https://github.com/ivalab/grasp_multiObject](https://github.com/ivalab/grasp_multiObject)
Image Source: [https://github.com/ivalab/grasp_multiObject](https://github.com/ivalab/grasp_multiObject)

Introducing paper: Real-world multiobject, multigrasp detection
OFEQ-10k

The **OFEQ-10k** dataset contains 12,548 detailed questions with corresponding math headlines from MathOverflow.

Source: [https://arxiv.org/pdf/1912.00839.pdf](https://arxiv.org/pdf/1912.00839.pdf)

Introducing paper: Automatic Generation of Headlines for Online Math Questions
Pascal Panoptic Parts

The Pascal Panoptic Parts dataset consists of annotations for the part-aware panoptic segmentation task on the PASCAL VOC 2010 dataset. It is created by merging scene-level labels from PASCAL-Context with part-level labels from PASCAL-Part

Source: [https://arxiv.org/abs/2106.06351](https://arxiv.org/abs/2106.06351s)
Image Source: [https://github.com/tue-mps/panoptic_parts](https://github.com/tue-mps/panoptic_parts)

Introducing paper: Part-aware Panoptic Segmentation
JSS Dataset
Full name: Jejueo Single Speaker Speech

The **Jejueo Single Speaker Speech** (JSS) dataset consists of 10k high-quality audio files recorded by a native Jejueo speaker and a transcript file.

Source: [https://arxiv.org/abs/1911.12071](https://arxiv.org/abs/1911.12071)

Introducing paper: Jejueo Datasets for Machine Translation and Speech Synthesis
needadvice

**needadvice** is a dataset for advice classification extracted from Reddit. In this dataset, posts are annotated for whether they contain advice or not. It contains 6,148 samples for training, 816 for validation and 898 for testing.

Source: [https://github.com/venkatasg/Advice-EMNLP2020](https://github.com/venkatasg/Advice-EMNLP2020)

Introducing paper: Help! Need Advice on Identifying Advice
FB1.5M

The **FB1.5M** dataset is a benchmark for Knowledge Graph Completion. It is based on Freebase and it contains 30 relations with less than 500 triplets as low-resource relations.

Source: [https://arxiv.org/pdf/1911.03091.pdf](https://arxiv.org/pdf/1911.03091.pdf)

Introducing paper: Relation Adversarial Network for Low Resource Knowledge Graph Completion
Wiki-zh

**Wiki-zh** is an annotated Chinese dataset for domain detection extracted from Wikipedia. It includes texts from 7 different domains: “Business and Commerce” (BUS), “Government and Politics” (GOV), “Physical and Mental Health” (HEA), “Law and Order” (LAW), “Lifestyle” (LIF), “Military” (MIL), and “General Purpose” (GEN). It contains 26,280 documents split into training, validation and test.

Source: [https://arxiv.org/pdf/1907.11499.pdf](https://arxiv.org/pdf/1907.11499.pdf)

Introducing paper: Weakly Supervised Domain Detection
Simulated Flying Shapes

The dataset consists of 90 000 grayscale videos that show two objects of equal shape and size in which one object approaches the other one. The object speed during the process of approaching is hereby modelled by a proportional-derivative controller. Overall, three different shapes (Rectangle, Triangle and Circle) are provided. Initial conﬁguration of the objects such as position and color were randomly sampled. Different from the moving MNIST dataset, the samples comprise a goal-oriented task, namely one object has to fully cover the other object rather than randomly moving, making it better suitable for testing prediction capabilities of an ML model.
For instance, one can use it as a toy dataset to investigate the capacity and output behavior of a deep neural network before testing it on real-world data.

Source: [https://github.com/ferreirafabio/FlyingShapesDataset](https://github.com/ferreirafabio/FlyingShapesDataset)
Image Source: [https://github.com/ferreirafabio/FlyingShapesDataset](https://github.com/ferreirafabio/FlyingShapesDataset)

Introducing paper: Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets
PKU-Reid

This dataset contains 114 individuals including 1824 images captured from two disjoint camera views. For each person, eight images are captured from eight different orientations under one camera view and are normalized to 128x48 pixels. This dataset is also split into two parts randomly. One contains 57 individuals for training, and the other contains 57 individuals for testing.

Source: [https://github.com/charliememory/PKU-Reid-Dataset](https://github.com/charliememory/PKU-Reid-Dataset)
Image Source: [https://arxiv.org/pdf/1605.02464.pdf](https://arxiv.org/pdf/1605.02464.pdf)

Introducing paper: Orientation Driven Bag of Appearances for Person Re-identification
YFCC100M Fine-Grained Geolocation

The **YFCC100M Fine-Grained Geolocation** dataset is a subset of 100 a set of 36,146 YFCC100M images that had Flickr tags that could be identified as corresponding to one of the labels in the iNaturalist 2017 dataset. The 36,146 images that were selected so have the following characteristics:
the image must have geolocation available,
the image must have at most one iNaturalist label,
at most ten examples were retained for each label.

Source: [https://github.com/visipedia/fg_geo](https://github.com/visipedia/fg_geo)
Image Source: [https://github.com/visipedia/fg_geo](https://github.com/visipedia/fg_geo)

Introducing paper: Geo-Aware Networks for Fine-Grained Recognition
AuxAI

**AuxAI** is a distantly supervised dataset for acronym identification.

Source: [https://github.com/PrimerAI/sdu-data](https://github.com/PrimerAI/sdu-data)

Introducing paper: Primer AI's Systems for Acronym Identification and Disambiguation