Omkar2118/datasets huggingface dataset:
STAR

A schema-guided task-oriented dialog dataset consisting of 127,833 utterances and knowledge base queries across 5,820 task-oriented dialogs in 13 domains that is especially designed to facilitate task and domain transfer learning in task-oriented dialog.

Source: [STAR: A Schema-Guided Dialog Dataset for Transfer Learning](/paper/star-a-schema-guided-dialog-dataset-for)

Introducing paper: STAR: A Schema-Guided Dialog Dataset for Transfer Learning
StereoMSI

StereoMSI comprises of 350 registered colour-spectral image pairs. The dataset has been used for the two tracks of the PIRM2018 challenge.

Source: [PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study](/paper/pirm2018-challenge-on-spectral-image-super)
Image Source: [Shoeiby et al](https://arxiv.org/pdf/1904.00540v2.pdf)

Introducing paper: PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study
stickerchart

The Stickerchat dataset is a large-scale real-world dialog dataset with stickers which contains 340K multi-turn dialog and sticker pairs.

Source: [https://arxiv.org/abs/2003.04679](https://arxiv.org/abs/2003.04679)

Introducing paper: Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog
Store dataset

The Store Dataset is a dataset for estimating 3D poses of multiple humans in real-time. It is captured inside two kinds of simulated stores with 12 and 28 cameras, respectively.

Source: [https://arxiv.org/abs/2003.03972](https://arxiv.org/abs/2003.03972)

Introducing paper: Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS
Story Commonsense

Story Commonsense is a new large-scale dataset with rich low-level annotations and establishes baseline performance on several new tasks, suggesting avenues for future research.

Source: [Modeling Naive Psychology of Characters in Simple Commonsense Stories](/paper/modeling-naive-psychology-of-characters-in)

Introducing paper: Modeling Naive Psychology of Characters in Simple Commonsense Stories
Stream-51

A new dataset for streaming classification consisting of temporally correlated images from 51 distinct object categories and additional evaluation classes outside of the training distribution to test novelty recognition. 

Source: [Stream-51: Streaming Classification and Novelty Detection from Videos](/paper/stream-51-streaming-classification-and)

Introducing paper: Stream-51: Streaming Classification and Novelty Detection from Videos
Street Dataset

A real-world image dataset that contains more than 900 images generated from 26 street cameras and 7 object categories annotated with detailed bounding box. The data distribution is non-IID and unbalanced, reflecting the characteristic real-world federated learning scenarios.

Source: [Real-World Image Datasets for Federated Learning](/paper/real-world-image-datasets-for-federated)

Introducing paper: Real-World Image Datasets for Federated Learning
Exact Street2Shop

A dataset containing 404,683 shop photos collected from 25 different online retailers and 20,357 street photos, providing a total of 39,479 clothing item matches between street and shop photos.

Source: [Where to Buy It: Matching Street Clothing Photos in Online Shops](/paper/where-to-buy-it-matching-street-clothing)

Introducing paper: Where to Buy It: Matching Street Clothing Photos in Online Shops
StreetHazards

StreetHazards is a synthetic dataset for anomaly detection, created by inserting a diverse array of foreign objects into driving scenes and re-render the scenes with these novel objects.

Source: [Scaling Out-of-Distribution Detection for Real-World Settings](/paper/a-benchmark-for-anomaly-segmentation)

Introducing paper: Scaling Out-of-Distribution Detection for Real-World Settings
StreetLearn

An interactive, first-person, partially-observed visual environment that uses Google Street View for its photographic content and broad coverage, and give performance baselines for a challenging goal-driven navigation task. 

Source: [The StreetLearn Environment and Dataset](/paper/the-streetlearn-environment-and-dataset)

Introducing paper: The StreetLearn Environment and Dataset
Street View Image, Pose, and 3D Cities Dataset

A large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information.

Source: [Generic 3D Representation via Pose Estimation and Matching](/paper/generic-3d-representation-via-pose-estimation)

Introducing paper: Generic 3D Representation via Pose Estimation and Matching
SubEdits

**SubEdits** is a human-annnoated post-editing dataset of neural machine translation outputs, compiled from in-house NMT outputs and human post-edits of subtitles form Rakuten Viki. It is collected from English-German annotations and contains 160k triplets.

Source: [https://github.com/shamilcm/pedra](https://github.com/shamilcm/pedra)
Image Source: [Chollampatt et al](https://arxiv.org/pdf/2009.14395v1.pdf)

Introducing paper: Can Automatic Post-Editing Improve NMT?
SubjQA

**SubjQA** is a question answering dataset that focuses on subjective (as opposed to factual) questions and answers. The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery, electronics, TripAdvisor (i.e. hotels), and restaurants. Each question is paired with a review and a span is highlighted as the answer to the question (with some questions having no answer). Moreover, both questions and answer spans are assigned a subjectivity label by annotators. Questions such as "How much does this product weigh?" is a factual question (i.e., low subjectivity), while "Is this easy to use?" is a subjective question (i.e., high subjectivity).

Source: [https://github.com/megagonlabs/SubjQA](https://github.com/megagonlabs/SubjQA)

Introducing paper: SubjQA: A Dataset for Subjectivity and Review Comprehension
Surveillance Camera Fight Dataset

The dataset is collected from the Youtube videos that contains fight instances in it. Also, some non-fight sequences from regular surveillance camera videos are included.
* There are 300 videos in total as 150 fight + 150 non-fight
* Videos are 2-second long
* Only the fight related parts are included in the samples

Source: [https://github.com/sayibet/fight-detection-surv-dataset](https://github.com/sayibet/fight-detection-surv-dataset)
Image Source: [https://github.com/sayibet/fight-detection-surv-dataset](https://github.com/sayibet/fight-detection-surv-dataset)

Introducing paper: Vision-based Fight Detection from Surveillance Cameras
SuspectGuilt Corpus

A corpus of annotated crime stories from English-language newspapers in the U.S. For SuspectGuilt, annotators read short crime articles and provided text-level ratings concerning the guilt of the main suspect as well as span-level annotations indicating which parts of the story they felt most influenced their ratings. SuspectGuilt thus provides a rich picture of how linguistic choices affect subjective guilt judgments.

Source: [Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives](/paper/modeling-subjective-assessments-of-guilt-in)

Introducing paper: Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives
SVD
Full name: Short Video Dataset

SVD is a large-scale short video dataset, which contains over 500,000 short videos collected from http://www.douyin.com and over 30,000 labeled pairs of near-duplicate videos.

Source: [SVD: A Large-Scale Short Video Dataset for Near-Duplicate Video Retrieval](/paper/svd-a-large-scale-short-video-dataset-for)

Introducing paper: SVD: A Large-Scale Short Video Dataset for Near-Duplicate Video Retrieval
SVIRO
Full name: Synthetic Vehicle Interior Rear Seat Occupancy Dataset

Contains bounding boxes for object detection, instance segmentation masks, keypoints for pose estimation and depth images for each synthetic scenery as well as images for each individual seat for classification. 

Source: [SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark](/paper/sviro-synthetic-vehicle-interior-rear-seat)

Introducing paper: SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark
SWAX
Full name: Sense Wax Attack dataset

Comprised of real human and wax figure images and videos that endorse the problem of face spoofing detection. The dataset consists of more than 1800 face images and 110 videos of 55 people/waxworks, arranged in training, validation and test sets with a large range in expression, illumination and pose variations. 

Source: [The SWAX Benchmark: Attacking Biometric Systems with Wax Figures](/paper/the-swax-benchmark-attacking-biometric)

Introducing paper: The SWAX Benchmark: Attacking Biometric Systems with Wax Figures
SweetRS

Uses a  platform with 77 candies and sweets to rank. Over 2000 users submitted over 44000 grades resulting in a matrix with 28% coverage.

Source: [SweetRS: Dataset for a recommender systems of sweets](/paper/sweetrs-dataset-for-a-recommender-systems-of)

Introducing paper: SweetRS: Dataset for a recommender systems of sweets
Swiss3DCities

Swiss3DCities is a dataset that is manually annotated for semantic segmentation with per-point labels, and is built using photogrammetry from images acquired by multirotors equipped with high-resolution cameras.

Source: [Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset](/paper/semantic-segmentation-on-swiss3dcities-a)
Image Source: [Can et al](https://arxiv.org/pdf/2012.12996.pdf)

Introducing paper: Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset
SynthCity

**SynthCity** is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is assigned a label from one of nine categories.

Source: [SynthCity: A large scale synthetic point cloud](/paper/synthcity-a-large-scale-synthetic-point-cloud)
Image Source: [http://www.synthcity.xyz/](http://www.synthcity.xyz/)

Introducing paper: SynthCity: A large scale synthetic point cloud
Synthetic Human Model Dataset

A synthetic dataset for evaluating non-rigid 3D human reconstruction based on conventional RGB-D cameras. The dataset consist of seven motion sequences of a single human model. 

Source: [Synthetic Human Model Dataset for Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction](/paper/synthetic-human-model-dataset-for-skeleton)

Introducing paper: Synthetic Human Model Dataset for Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
Synthetic Keystroke

This dataset is a large-scale synthetic dataset to simulate the attack scenario for a keystroke inference attack.

Source: [https://arxiv.org/abs/2009.05796](https://arxiv.org/abs/2009.05796)

Introducing paper: Revisiting the Threat Space for Vision-based Keystroke Inference Attacks
SYNTHIA-AL

Specially designed to evaluate active learning for video object detection in road scenes. 

Source: [Temporal Coherence for Active Learning in Videos](/paper/temporal-coherence-for-active-learning-in)

Introducing paper: Temporal Coherence for Active Learning in Videos
Synthinel-1

**Synthinel-1** is a collection of synthetic overhead imagery with full pixel-wise building segmentation labels.

Source: [https://github.com/timqqt/Synthinel](https://github.com/timqqt/Synthinel)
Image Source: [https://github.com/timqqt/Synthinel](https://github.com/timqqt/Synthinel)

Introducing paper: The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation
SYSU-30k

**SYSU-30k** contains 30k categories of persons, which is about 20 times larger than CUHK03 (1.3k categories) and Market1501 (1.5k categories), and 30 times larger than ImageNet (1k categories). SYSU-30k contains 29,606,918 images. Moreover, SYSU-30k provides not only a large platform for the weakly supervised ReID problem but also a more challenging test set that is consistent with the realistic setting for standard evaluation.

Source: [https://github.com/wanggrun/SYSU-30k](https://github.com/wanggrun/SYSU-30k)
Image Source: [https://github.com/wanggrun/SYSU-30k](https://github.com/wanggrun/SYSU-30k)

Introducing paper: Weakly Supervised Person Re-ID: Differentiable Graphical Learning and A New Benchmark
SYSU-CEUS

The **SYSU-CEUS** dataset consists of three types of Focal liver lesions (FLLs): 186 HCC instances, 109 HEM instances and 58 FNH instances (i.e.,186 malignant instances and 167 benign instances).
This dataset is collected from the First Affiliated Hospital, Sun Yat-sen University. The equipment used was Aplio SSA-770A (Toshiba Medical System).
All these instances with resolution 768*576 were taken from different patients, with large variations in appearance and enhancement patterns (e.g. sizes, contrasts, shapes and locations) of the FLLs.

Source: [https://github.com/lemondan/Focal-liver-lesions-dataset-in-CEUS](https://github.com/lemondan/Focal-liver-lesions-dataset-in-CEUS)

Introducing paper: Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model
TACO

**TACO** is a growing image dataset of waste in the wild. It contains images of litter taken under diverse environments: woods, roads and beaches. These images are manually labelled and segmented according to a hierarchical taxonomy to train and evaluate object detection algorithms. The annotations are provided in COCO format.

Source: [https://github.com/pedropro/TACO](https://github.com/pedropro/TACO)
Image Source: [https://github.com/pedropro/TACO](https://github.com/pedropro/TACO)

Introducing paper: TACO: Trash Annotations in Context for Litter Detection
TACoS Multi-Level Corpus

Augments the video-description dataset TACoS with short and single sentence descriptions.

Source: [Coherent Multi-Sentence Video Description with Variable Level of Detail](/paper/coherent-multi-sentence-video-description)

Introducing paper: Coherent Multi-Sentence Video Description with Variable Level of Detail
Talk2Car

The **Talk2Car** dataset finds itself at the intersection of various research domains, promoting the development of cross-disciplinary solutions for improving the state-of-the-art in grounding natural language into visual space. The annotations were gathered with the following aspects in mind:
Free-form high quality natural language commands, that stimulate the development of solutions that can operate in the wild.
A realistic task setting. Specifically, the authors consider an autonomous driving setting, where a passenger can control the actions of an Autonomous Vehicle by giving commands in natural language.
The Talk2Car dataset was build on top of the nuScenes dataset to include an extensive suite of sensor modalities, i.e. semantic maps, GPS, LIDAR, RADAR and 360-degree RGB images annotated with 3D bounding boxes. Such variety of input modalities sets the object referral task on the Talk2Car dataset apart from related challenges, where additional sensor modalities are generally missing.

Source: [https://talk2car.github.io/](https://talk2car.github.io/)
Image Source: [https://github.com/talk2car/Talk2Car](https://github.com/talk2car/Talk2Car)

Introducing paper: Talk2Car: Taking Control of Your Self-Driving Car
Talk2Nav

Talk2Nav is a large-scale dataset with verbal navigation instructions.

Source: [Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory](/paper/talk2nav-long-range-vision-and-language)
Image Source: [https://www.trace.ethz.ch/publications/2019/talk2nav/index.html](https://www.trace.ethz.ch/publications/2019/talk2nav/index.html)

Introducing paper: Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory
TalkDown

**TalkDown** is a labelled dataset for condescension detection in context. The dataset is derived from Reddit, a set of online communities that is diverse in content and tone. The dataset is built from COMMENT and REPLY pairs in which the REPLY targets a specific quoted span (QUOTED) in the COMMENT as being condescending. The dataset contains 3,255 positive (condescend) samples and 3,255 negative ones.

Source: [https://arxiv.org/pdf/1909.11272.pdf](https://arxiv.org/pdf/1909.11272.pdf)

Introducing paper: TalkDown: A Corpus for Condescension Detection in Context
TAO
Full name: Tracking Any Object Dataset

TAO is a federated dataset for Tracking Any Object, containing 2,907 high resolution videos, captured in diverse environments, which are half a minute long on average. A bottom-up approach was used for discovering a large vocabulary of 833 categories, an order of magnitude more than prior tracking benchmarks. 

The dataset was annotated by labelling tracks for objects that move at any point in the video, and giving names to them post factum. 

Source: [TAO: A Large-Scale Benchmark for Tracking Any Object](/paper/tao-a-large-scale-benchmark-for-tracking-any)

Introducing paper: TAO: A Large-Scale Benchmark for Tracking Any Object
TaoDescribe

The **TaoDescribe** dataset contains 2,129,187 product titles and descriptions in Chinese.

Source: [https://github.com/qibinc/KOBE](https://github.com/qibinc/KOBE)

Introducing paper: Towards Knowledge-Based Personalized Product Description Generation in E-commerce