dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
SG-NLG | The SG-NLG dataset is a pre-processed version of the [DSTC8 Schema-Guided Dialogue SGD dataset](https://paperswithcode.com/dataset/sgd), designed specifically for data-to-text Natural Language Generation (NLG). The original DSTC8 SGD contains ~20,000 dialogues spanning across ~20 domains.
This SG-NLG dataset is designed to make it easier to conduct NLG experiments on the SGD data. It consists of pre-processed SGD data by pairing the schema for each system turn with the corresponding set of natural language strings that realize it. It also “delexicalizes” the prompts (replace related values with fixed names) to convert them into templates that make them more generic for use within a dialog system.
The final SG-NLG dataset is composed of nearly 4K MRs and over 140K templates. | Provide a detailed description of the following dataset: SG-NLG |
ShapenetRender | **ShapenetRender**er is an extension of the ShapeNet Core dataset which has more variation in camera angles. For each mesh model, the dataset provides 36 views with smaller variation and 36 views with larger variation. The resolution of the newly rendered images is 224x224 in contrast to the 137x137 original resolution. Additionally, each RGB image is paired with a depth image, a normal map and an albedo image.
Source: [https://github.com/Xharlie/ShapenetRender_more_variation](https://github.com/Xharlie/ShapenetRender_more_variation)
Image Source: [https://github.com/Xharlie/ShapenetRender_more_variation](https://github.com/Xharlie/ShapenetRender_more_variation) | Provide a detailed description of the following dataset: ShapenetRender |
ShapeNet-Skeleton | The **ShapeNet-Skeleton** dataset has ground-truth skeleton point sets and skeletal volumes for object instances in the ShapeNet dataset.
Source: [https://arxiv.org/pdf/2008.05742.pdf](https://arxiv.org/pdf/2008.05742.pdf) | Provide a detailed description of the following dataset: ShapeNet-Skeleton |
3D Shapes Dataset | 3dshapes is a dataset of 3D shapes procedurally generated from 6 ground truth independent latent factors. These factors are floor colour, wall colour, object colour, scale, shape and orientation. | Provide a detailed description of the following dataset: 3D Shapes Dataset |
ShapeStacks | A simulation-based dataset featuring 20,000 stack configurations composed of a variety of elementary geometric primitives richly annotated regarding semantics and structural stability. | Provide a detailed description of the following dataset: ShapeStacks |
ShEMO | The database includes 3000 semi-natural utterances, equivalent to 3 hours and 25 minutes of speech data extracted from online radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including anger, fear, happiness, sadness and surprise, as well as neutral state. | Provide a detailed description of the following dataset: ShEMO |
SHIDC-BC-Ki-67 | Benchmark for BC Ki-67 stained cell detection and further annotated classification of cells. | Provide a detailed description of the following dataset: SHIDC-BC-Ki-67 |
ShopSign | A newly developed natural scene text dataset of Chinese shop signs in street views. | Provide a detailed description of the following dataset: ShopSign |
SIDD | SIDD is an image denoising dataset containing 30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras. Ground truth images are provided along with the noisy images. | Provide a detailed description of the following dataset: SIDD |
SidechainNet | **SidechainNet** is a protein structure prediction dataset that directly extends ProteinNet. Specifically, SidechainNet adds measurements for protein angles and coordinates that describe the complete, all-atom protein structure (backbone and sidechain, excluding hydrogens) instead of the protein backbone alone.
Source: [https://github.com/jonathanking/sidechainnet](https://github.com/jonathanking/sidechainnet) | Provide a detailed description of the following dataset: SidechainNet |
SIDOD | SIDOD is a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset) and flying distractors. | Provide a detailed description of the following dataset: SIDOD |
Simitate | **Simitate** is a hybrid benchmarking suite targeting the evaluation of approaches for imitation learning. It consists on a dataset containing 1938 sequences where humans perform daily activities in a realistic environment. The dataset is strongly coupled with an integration into a simulator. RGB and depth streams with a resolution of 960×540 at 30Hz and accurate ground truth poses for the demonstrator's hand, as well as the object in 6 DOF at 120Hz are provided. Along with the dataset the 3D model of the used environment and labelled object images are also provided.
Source: [https://arxiv.org/abs/1905.06002](https://arxiv.org/abs/1905.06002)
Image Source: [https://github.com/raphaelmemmesheimer/simitate](https://github.com/raphaelmemmesheimer/simitate) | Provide a detailed description of the following dataset: Simitate |
SIMMC | Situated Interactive MultiModal Conversations (**SIMMC**) is the task of taking multimodal actions grounded in a co-evolving multimodal input content in addition to the dialog history. This dataset contains two SIMMC datasets totalling ~13K human-human dialogs (~169K utterances) using a multimodal Wizard-of-Oz (WoZ) setup, on two shopping domains: (a) furniture (grounded in a shared virtual environment) and (b) fashion (grounded in an evolving set of images).
Source: [https://github.com/facebookresearch/simmc](https://github.com/facebookresearch/simmc)
Image Source: [https://github.com/facebookresearch/simmc](https://github.com/facebookresearch/simmc) | Provide a detailed description of the following dataset: SIMMC |
simply-CLEVR | The **simply-CLEVR** dataset aims to provide a benchmark dataset that can be used for transparent quantitative evaluation of explanation methods (aka heatmaps/XAI methods).
It is made of simple Visual Question Answering (VQA) questions, which are derived from the original CLEVR task, and where each question is accompanied by two Ground Truth Masks that serve as a basis for evaluating explanations on the input image.
Source: [https://github.com/ahmedmagdiosman/simply-clevr-dataset](https://github.com/ahmedmagdiosman/simply-clevr-dataset)
Image Source: [https://github.com/ahmedmagdiosman/simply-clevr-dataset](https://github.com/ahmedmagdiosman/simply-clevr-dataset) | Provide a detailed description of the following dataset: simply-CLEVR |
SIS | Comprises of 400 naturalistic usages of literature-informed verbs spanning the spectrum of symmetry-asymmetry. | Provide a detailed description of the following dataset: SIS |
SI-SCORE | A synthetic dataset uses for a systematic analysis across common factors of variation. | Provide a detailed description of the following dataset: SI-SCORE |
SIZER | Dataset of clothing size variation which includes different subjects wearing casual clothing items in various sizes, totaling to approximately 2000 scans. This dataset includes the scans, registrations to the SMPL model, scans segmented in clothing parts, garment category and size labels. | Provide a detailed description of the following dataset: SIZER |
SketchGraphs | **SketchGraphs** is a dataset of 15 million sketches extracted from real-world CAD models intended to facilitate research in both ML-aided design and geometric program induction.
Each sketch is represented as a geometric constraint graph where edges denote designer-imposed geometric relationships between primitives, the nodes of the graph.
Source: [https://github.com/PrincetonLIPS/SketchGraphs](https://github.com/PrincetonLIPS/SketchGraphs)
Image Source: [https://github.com/PrincetonLIPS/SketchGraphs](https://github.com/PrincetonLIPS/SketchGraphs) | Provide a detailed description of the following dataset: SketchGraphs |
ShoeV2 | **ShoeV2** is a dataset of 2,000 photos and 6648 sketches of shoes. The dataset is designed for fine-grained sketch-based image retrieval. | Provide a detailed description of the following dataset: ShoeV2 |
Skill2vec | Collects a huge number of job descriptions from Dice.com - one of the most popular career website about Tech jobs in USA. From these job descriptions, skills are extracted for each one by using skills dictionary. Now, the dataset is presented by a list of collections of skills based on job descriptions. After crawling, there are a total of 5GB with more than 1,400,000 job descriptions. From these data, skills are extracted and performed as a list of skills in the same context, the context here includes skills in the same job description. | Provide a detailed description of the following dataset: Skill2vec |
SKU110K-R | **SKU110K-R** is a dataset relabeled with oriented bounding boxes based on SKU110K. It is focused on evaluating oriented and densely packed object detection.
Source: [https://github.com/Anymake/DRN_CVPR2020](https://github.com/Anymake/DRN_CVPR2020)
Image Source: [https://github.com/Anymake/DRN_CVPR2020](https://github.com/Anymake/DRN_CVPR2020) | Provide a detailed description of the following dataset: SKU110K-R |
SlowFlow | **SlowFlow** is an optical flow dataset collected by applying Slow Flow technique on data from a high-speed camera and analyzing the performance of the state-of-the-art in optical flow under various levels of motion blur. | Provide a detailed description of the following dataset: SlowFlow |
SLURP | A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets. | Provide a detailed description of the following dataset: SLURP |
SMHD | A novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. | Provide a detailed description of the following dataset: SMHD |
SmokEng | SmokEng is a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet. | Provide a detailed description of the following dataset: SmokEng |
SMS Spam Collection Data Set | This corpus has been collected from free or free for research sources at the Internet:
- A collection of 425 SMS spam messages was manually extracted from the Grumbletext Web site. This is a UK forum in which cell phone users make public claims about SMS spam messages, most of them without reporting the very spam message received. The identification of the text of spam messages in the claims is a very hard and time-consuming task, and it involved carefully scanning hundreds of web pages.
- A subset of 3,375 SMS randomly chosen ham messages of the NUS SMS Corpus (NSC), which is a dataset of about 10,000 legitimate messages collected for research at the Department of Computer Science at the National University of Singapore. The messages largely originate from Singaporeans and mostly from students attending the University. These messages were collected from volunteers who were made aware that their contributions were going to be made publicly available.
- A list of 450 SMS ham messages collected from Caroline Tag's PhD Thesis.
- the SMS Spam Corpus v.0.1 Big. It has 1,002 SMS ham messages and 322 spam messages. | Provide a detailed description of the following dataset: SMS Spam Collection Data Set |
SMS-WSJ | Spatialized Multi-Speaker Wall Street Journal (SMS-WSJ) consists of artificially mixed speech taken from the WSJ database, but unlike earlier databases this one considers all WSJ0+1 utterances and takes care of strictly separating the speaker sets present in the training, validation and test sets. | Provide a detailed description of the following dataset: SMS-WSJ |
SNLI-VE | Visual Entailment (VE) consists of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal of a trained VE model is to predict whether the image semantically entails the text. **SNLI-VE** is a dataset for VE which is based on the Stanford Natural Language Inference corpus and Flickr30k dataset.
Source: [https://github.com/necla-ml/SNLI-VE](https://github.com/necla-ml/SNLI-VE) | Provide a detailed description of the following dataset: SNLI-VE |
So2Sat LCZ42 | So2Sat LCZ42 consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months. | Provide a detailed description of the following dataset: So2Sat LCZ42 |
SOBA | 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. | Provide a detailed description of the following dataset: SOBA |
SoccerData | A dataset of 4562 images of which 4152 images contain a soccer ball. | Provide a detailed description of the following dataset: SoccerData |
SoccerDB | 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. | Provide a detailed description of the following dataset: SoccerDB |
SoccerNet | A benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours. A total of 6,637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution). | Provide a detailed description of the following dataset: SoccerNet |
SoccerNet-v2 | A novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. | Provide a detailed description of the following dataset: SoccerNet-v2 |
Social-IQ | Social-IQ is an unconstrained benchmark specifically designed to train and evaluate socially intelligent technologies. By providing a rich source of open-ended questions and answers, Social-IQ opens the door to explainable social intelligence. The dataset contains rigorously annotated and validated videos, questions and answers, as well as annotations for the complexity level of each question and answer. Social-IQ contains 1,250 natural in-the-wild social situations, 7,500 questions and 52,500 correct and incorrect answers. | Provide a detailed description of the following dataset: Social-IQ |
SMM4H | Social Media Mining for Health (SMM4H) Shared Task is a massive data source for biomedical and public health applications. | Provide a detailed description of the following dataset: SMM4H |
SoloDance | A large-scale HVMT dataset named SoloDance. | Provide a detailed description of the following dataset: SoloDance |
Some Like it Hoax | **Some Like it Hoax** is a fake news detection dataset consisting of 15,500 Facebook posts and 909,236 users. | Provide a detailed description of the following dataset: Some Like it Hoax |
SONYC-UST-V2 | A dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems for real-world urban noise monitoring. While datasets of urban recordings are available, this dataset provides the opportunity to investigate how spatiotemporal metadata can aid in the prediction of urban sound tags. SONYC-UST-V2 consists of 18510 audio recordings from the "Sounds of New York City" (SONYC) acoustic sensor network, including the timestamp of audio acquisition and location of the sensor. | Provide a detailed description of the following dataset: SONYC-UST-V2 |
SoyCultivarVein | The SoyCultivarVein dataset is a publicly available dataset, which comprises 100 categories (cultivars) with 6 samples (leaf images) in each cultivar and thus has a total number of 100×6 = 600 images (Yu et al. 2019). The leaves in the SoyCultivarVein dataset are highly similar due to the fact that they all belong to the same species, making it a new and challenging dataset for the artificial intelligence and pattern analysis research community. | Provide a detailed description of the following dataset: SoyCultivarVein |
SP-10K | A large-scale evaluation set that provides human ratings for the plausibility of 10,000 SP pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and adjectives in American English. | Provide a detailed description of the following dataset: SP-10K |
SpaceNet MVOI | An open source Multi-View Overhead Imagery dataset with 27 unique looks from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of these images cover the same 665 square km geographic extent and are annotated with 126,747 building footprint labels, enabling direct assessment of the impact of viewpoint perturbation on model performance. | Provide a detailed description of the following dataset: SpaceNet MVOI |
Spaceship Dataset | The Spaceship dataset is a dataset for evaluating agents’ ability to learn to solve a class of physics-based tasks. The tasks consist on a spaceship that has to reach a the mothership in 11 steps, in an environment where static planets exert gravitational forces on the spaceship, which induce complex non-linear dynamics on the motion over the 11 steps.
Source: [https://arxiv.org/pdf/1705.02670.pdf](https://arxiv.org/pdf/1705.02670.pdf) | Provide a detailed description of the following dataset: Spaceship Dataset |
SPair-71k | SPair-71k contains 70,958 image pairs with diverse variations in viewpoint and scale. Compared to previous datasets, it is significantly larger in number and contains more accurate and richer annotations. | Provide a detailed description of the following dataset: SPair-71k |
SPARE3D | Contains three types of 2D-3D reasoning tasks on view consistency, camera pose, and shape generation, with increasing difficulty. | Provide a detailed description of the following dataset: SPARE3D |
SpatialSense Benchmark | SpatialSense Benchmark is a dataset specializing in spatial relation recognition which captures a broad spectrum of such challenges, allowing for proper benchmarking of computer vision techniques. | Provide a detailed description of the following dataset: SpatialSense Benchmark |
SpeakingFaces | 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. | Provide a detailed description of the following dataset: SpeakingFaces |
SpectroVision | **SpectroVision** is a dataset of 14,400 high resolution texture images and spectral measurements collected from a PR2 mobile manipulator that interacted with 144 household objects from eight material categories.
Source: [https://github.com/Healthcare-Robotics/spectrovision](https://github.com/Healthcare-Robotics/spectrovision)
Image Source: [https://github.com/Healthcare-Robotics/spectrovision](https://github.com/Healthcare-Robotics/spectrovision) | Provide a detailed description of the following dataset: SpectroVision |
SPEECH-COCO | SPEECH-COCO contains speech captions that are generated using text-to-speech (TTS) synthesis resulting in 616,767 spoken captions (more than 600h) paired with images. | Provide a detailed description of the following dataset: SPEECH-COCO |
SPIRS | A first-of-its-kind large dataset of sarcastic/non-sarcastic tweets with high-quality labels and extra features: (1) sarcasm perspective labels (2) new contextual features. The dataset is expected to advance sarcasm detection research. | Provide a detailed description of the following dataset: SPIRS |
SPLASH | A dataset of utterances, incorrect SQL interpretations and the corresponding natural language feedback. | Provide a detailed description of the following dataset: SPLASH |
Spoken-SQuAD | 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.
Source: [https://github.com/chiahsuan156/Spoken-SQuAD](https://github.com/chiahsuan156/Spoken-SQuAD)
Image Source: [https://github.com/chiahsuan156/Spoken-SQuAD](https://github.com/chiahsuan156/Spoken-SQuAD) | Provide a detailed description of the following dataset: Spoken-SQuAD |
Spotify Podcast | A set of approximately 100K podcast episodes comprised of raw audio files along with accompanying ASR transcripts. This represents over 47,000 hours of transcribed audio, and is an order of magnitude larger than previous speech-to-text corpora. | Provide a detailed description of the following dataset: Spotify Podcast |
SQuAD-es | Stanford Question Answering Dataset (SQuAD) into Spanish. | Provide a detailed description of the following dataset: SQuAD-es |
SQuAD-it | SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. | Provide a detailed description of the following dataset: SQuAD-it |
SQuAD-shifts | 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. | Provide a detailed description of the following dataset: SQuAD-shifts |
SQUID | A dataset of images taken in different locations with varying water properties, showing color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images. | Provide a detailed description of the following dataset: SQUID |
STAIR Actions Captions | A large-scale Japanese video caption dataset consisting of 79,822 videos and 399,233 captions. Each caption in the dataset describes a video in the form of "who does what and where." | Provide a detailed description of the following dataset: STAIR Actions Captions |
Standardized Project Gutenberg Corpus | The **Standardized Project Gutenberg Corpus** (SPGC) is an open science approach to a curated version of the complete PG data containing more than 50,000 books and more than 3×109 word-tokens.
Source: [https://arxiv.org/abs/1812.08092](https://arxiv.org/abs/1812.08092) | Provide a detailed description of the following dataset: Standardized Project Gutenberg Corpus |
StanfordExtra | An 'in the wild' dataset of 20,580 dog images for which 2D joint and silhouette annotations were collected. | Provide a detailed description of the following dataset: StanfordExtra |
StaQC | **StaQC** (Stack Overflow Question-Code pairs) is a large dataset of around 148K Python and 120K SQL domain question-code pairs, which are automatically mined from StackOverflow. | Provide a detailed description of the following dataset: StaQC |
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. | Provide a detailed description of the following dataset: STAR |
StereoMSI | StereoMSI comprises of 350 registered colour-spectral image pairs. The dataset has been used for the two tracks of the PIRM2018 challenge. | Provide a detailed description of the following dataset: StereoMSI |
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) | Provide a detailed description of the following dataset: stickerchart |
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) | Provide a detailed description of the following dataset: Store dataset |
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. | Provide a detailed description of the following dataset: Story Commonsense |
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. | Provide a detailed description of the following dataset: Stream-51 |
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. | Provide a detailed description of the following dataset: Street Dataset |
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. | Provide a detailed description of the following dataset: Exact Street2Shop |
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. | Provide a detailed description of the following dataset: StreetHazards |
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. | Provide a detailed description of the following dataset: StreetLearn |
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. | Provide a detailed description of the following dataset: Street View Image, Pose, and 3D Cities Dataset |
Structured3D | **Structured3D** is a large-scale photo-realistic dataset containing 3.5K house designs (a) created by professional designers with a variety of ground truth 3D structure annotations (b) and generate photo-realistic 2D images (c).
The dataset consists of rendering images and corresponding ground truth annotations (e.g., semantic, albedo, depth, surface normal, layout) under different lighting and furniture configurations. | Provide a detailed description of the following dataset: Structured3D |
ST-VQA | ST-VQA aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the VQA process. | Provide a detailed description of the following dataset: ST-VQA |
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. | Provide a detailed description of the following dataset: SubEdits |
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) | Provide a detailed description of the following dataset: SubjQA |
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) | Provide a detailed description of the following dataset: Surveillance Camera Fight Dataset |
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. | Provide a detailed description of the following dataset: SuspectGuilt Corpus |
SVD | 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. | Provide a detailed description of the following dataset: SVD |
SVIRO | 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. | Provide a detailed description of the following dataset: SVIRO |
SWAX | 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. | Provide a detailed description of the following dataset: SWAX |
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. | Provide a detailed description of the following dataset: SweetRS |
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. | Provide a detailed description of the following dataset: Swiss3DCities |
Synscapes | Synscapes is a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis. | Provide a detailed description of the following dataset: Synscapes |
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. | Provide a detailed description of the following dataset: SynthCity |
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. | Provide a detailed description of the following dataset: Synthetic Human Model Dataset |
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) | Provide a detailed description of the following dataset: Synthetic Keystroke |
SYNTHIA-AL | Specially designed to evaluate active learning for video object detection in road scenes. | Provide a detailed description of the following dataset: SYNTHIA-AL |
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) | Provide a detailed description of the following dataset: Synthinel-1 |
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) | Provide a detailed description of the following dataset: SYSU-30k |
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) | Provide a detailed description of the following dataset: SYSU-CEUS |
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) | Provide a detailed description of the following dataset: TACO |
TACoS Multi-Level Corpus | Augments the video-description dataset TACoS with short and single sentence descriptions. | Provide a detailed description of the following dataset: TACoS Multi-Level Corpus |
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. | Provide a detailed description of the following dataset: Talk2Car |
Talk2Nav | Talk2Nav is a large-scale dataset with verbal navigation instructions. | Provide a detailed description of the following dataset: Talk2Nav |
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) | Provide a detailed description of the following dataset: TalkDown |
Talk the Walk | Talk The Walk is a large-scale dialogue dataset grounded in
action and perception. The task involves two agents (a “guide” and a “tourist”)
that communicate via natural language in order to achieve a common goal: having
the tourist navigate to a given target location. | Provide a detailed description of the following dataset: Talk the Walk |
TAO | 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. | Provide a detailed description of the following dataset: TAO |
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) | Provide a detailed description of the following dataset: TaoDescribe |
TaPaCo | TaPaCo is a freely available paraphrase corpus for 73 languages extracted from the Tatoeba database. | Provide a detailed description of the following dataset: TaPaCo |
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