Omkar2118/datasets huggingface dataset:
3D Ken Burns Dataset

Provides a large-scale synthetic dataset which contains accurate ground truth depth of various photo-realistic scenes.

Source: [3D Ken Burns Effect from a Single Image](/paper/3d-ken-burns-effect-from-a-single-image)

Introducing paper: 3D Ken Burns Effect from a Single Image
3DMAD
Full name: 3D Mask Attack Dataset

The 3D Mask Attack Database (3DMAD) is a biometric (face) spoofing database. It currently contains 76500 frames of 17 persons, recorded using Kinect for both real-access and spoofing attacks. Each frame consists of:

- a depth image (640x480 pixels – 1x11 bits)
- the corresponding RGB image (640x480 pixels – 3x8 bits)
- manually annotated eye positions (with respect to the RGB image).

Source: [3D Mask Attack Dataset](https://www.idiap.ch/dataset/3dmad)
3DSeg-8

The 3DSeg-8 is a collection of several publicly available 3D segmentation datasets from different medical imaging modalities, e.g. magnetic resonance imaging (MRI) and computed tomography (CT), with various scan regions, target organs and pathologies.

Introducing paper: Med3D: Transfer Learning for 3D Medical Image Analysis
3D-ZeF
Full name: 3D ZebraFish Tracking Benchmark

**3D-ZeF** dataset consists of eight sequences with a duration between 15-120 seconds and 1-10 free moving zebrafish. The videos have been annotated with a total of 86,400 points and bounding boxes.

Source: [3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset](/paper/3d-zef-a-3d-zebrafish-tracking-benchmark-1)

Introducing paper: 3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset
3RScan

A novel dataset and benchmark, which features 1482 RGB-D scans of 478 environments across multiple time steps. Each scene includes several objects whose positions change over time, together with ground truth annotations of object instances and their respective 6DoF mappings among re-scans. 

Source: [RIO: 3D Object Instance Re-Localization in Changing Indoor Environments](/paper/rio-3d-object-instance-re-localization-in)

Introducing paper: RIO: 3D Object Instance Re-Localization in Changing Indoor Environments
4Seasons

4Seasons is adataset covering seasonal and challenging perceptual conditions for autonomous driving.

Source: [4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving](/paper/4seasons-a-cross-season-dataset-for-multi)
Image Source: [Wenzel et al](https://arxiv.org/pdf/2009.06364v2.pdf)

Introducing paper: 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
A*3D

The **A*3D** dataset is a step forward to make autonomous driving safer for pedestrians and the public in the real world.
Characteristics:
* 230K human-labeled 3D object annotations in 39,179 LiDAR point cloud frames and corresponding frontal-facing RGB images.
* Captured at different times (day, night) and weathers (sun, cloud, rain).

Source: [https://github.com/I2RDL2/ASTAR-3D](https://github.com/I2RDL2/ASTAR-3D)
Image Source: [https://github.com/I2RDL2/ASTAR-3D](https://github.com/I2RDL2/ASTAR-3D)

Introducing paper: A*3D Dataset: Towards Autonomous Driving in Challenging Environments
AAVE/SAE Paired Dataset

AAVE/SAE Paired Dataset contains 2019 intent-equivalent AAVE/SAE pairs. The AAVE (African-American Vernacular English) samples are sampled from Blodgett et. al. (2016)'s TwitterAAE, with their corresponding SAE (Standard American English) samples annotated by Amazon MTurk.

Source: [https://github.com/sophiegroenwold/AAVE_SAE_dataset](https://github.com/sophiegroenwold/AAVE_SAE_dataset)

Introducing paper: Investigating African-American Vernacular English in Transformer-Based Text Generation
ACL ARC

ACL Anthology Reference Corpus (ACL ARC) is a collection of 10,920 academic papers from the ACL Anthology. ACL ARC is cleaned to remove:

- files that look like not full papers, paper fragments, foreign-language papers (e.g., French), or pure junk.
- headers (title and author information; NOT abstract).
- footers ("References" line and the actual references).
- some bad characters (spurious characters).
- some page numbers (i.e., a single number appearing on a line, with nothing else attached to it).
- significant foreign-language (e.g., French) content in an otherwise English paper.

The cleaned corpus has 10,628 documents.

Source: [ACL ARC](https://web.eecs.umich.edu/~lahiri/acl_arc.html)
ACRONYM

A dataset for robot grasp planning based on physics simulation. The dataset contains 17.7M parallel-jaw grasps, spanning 8872 objects from 262 different categories, each labeled with the grasp result obtained from a physics simulator. 

Source: [ACRONYM: A Large-Scale Grasp Dataset Based on Simulation](/paper/acronym-a-large-scale-grasp-dataset-based-on)

Introducing paper: ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
Acronym Identification

Is an acronym disambiguation (AD) dataset for scientific domain with 62,441 samples which is significantly larger than the previous scientific AD dataset.

Source: [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](/paper/what-does-this-acronym-mean-introducing-a-new)

Introducing paper: What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation
ActioNet

**ActioNet** is a video task-based dataset collected in a synthetic 3D environment. It contains 3,038 annotated videos and hierarchical task structures over 65 individual household tasks from 120 different scenes. Each task is annotated across three to five different scenes by 10 different annotators. The tasks can be broken down into four categories: living room, bedroom, bathroom, kitchen.

Source: [https://github.com/SamsonYuBaiJian/actionet](https://github.com/SamsonYuBaiJian/actionet)

Introducing paper: Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment
ActivityNet Entities

ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase. This allows training video description models with this data, and importantly, evaluate how grounded or "true" such model are to the video they describe.

Source: [https://github.com/facebookresearch/ActivityNet-Entities](https://github.com/facebookresearch/ActivityNet-Entities)
Image Source: [https://github.com/facebookresearch/ActivityNet-Entities](https://github.com/facebookresearch/ActivityNet-Entities)

Introducing paper: Grounded Video Description
ActivityNet-QA

The ActivityNet-QA dataset contains 58,000 human-annotated QA pairs on 5,800 videos derived from the popular ActivityNet dataset. The dataset provides a benchmark for testing the performance of VideoQA models on long-term spatio-temporal reasoning.

Source: [ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering](/paper/activitynet-qa-a-dataset-for-understanding)

Introducing paper: ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering
ActivityNet Thumbnails

Consists of 10,000+ video-sentence pairs with each accompanied by an annotated sentence specified video thumbnail. 

Source: [Sentence Specified Dynamic Video Thumbnail Generation](/paper/sentence-specified-dynamic-video-thumbnail)

Introducing paper: Sentence Specified Dynamic Video Thumbnail Generation
ADHA

ADHA: “Adverbs Describing Human Actions” is the first benchmark for a new problem — recognizing human action adverbs (HAA). This is the first step for computer vision to change over from pattern recognition to real AI. Some key features of ADHA are: a semantically complete set of adverbs describing human actions, a set of common, describable human actions, and an exhaustive labeling of simultaneously emerging actions in each video.

Source: [ADHA](http://www.mvig.org/research/adha/adha.html)
ADL Piano MIDI

The **ADL Piano MIDI** is a dataset of 11,086 piano pieces from different genres. This dataset is based on the Lakh MIDI dataset, which is a collection on 45,129 unique MIDI files that have been matched to entries in the Million Song Dataset. Most pieces in the Lakh MIDI dataset have multiple instruments, so for each file the authors of ADL Piano MIDI dataset extracted only the tracks with instruments from the "Piano Family" (MIDI program numbers 1-8). This process generated a total of 9,021 unique piano MIDI files. Theses 9,021 files were then combined with other approximately 2,065 files scraped from publicly-available sources on the internet. All the files in the final collection were de-duped according to their MD5 checksum.

Source: [ADL Piano MIDI](https://github.com/lucasnfe/adl-piano-midi)

Introducing paper: Computer-Generated Music for Tabletop Role-Playing Games
Advice Seeking Questions

The Advice-Seeking Questions (ASQ) dataset is a collection of personal narratives with advice-seeking questions. The dataset has been split into train, test, heldout sets, with 8865, 2500, 10000 test instances each. This dataset is used to train and evaluate methods that can infer what is the advice-seeking goal behind a personal narrative. This task is formulated as a cloze test, where the goal is to identify which of two advice-seeking questions was removed from a given narrative.

Source: [https://github.com/CornellNLP/ASQ](https://github.com/CornellNLP/ASQ)

Introducing paper: Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives
ADVIO

Provides a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track. 

Source: [ADVIO: An authentic dataset for visual-inertial odometry](/paper/advio-an-authentic-dataset-for-visual)

Introducing paper: ADVIO: An authentic dataset for visual-inertial odometry
AeroRIT

AeroRIT is a hyperspectral dataset to facilitate aerial hyperspectral scene understanding.

Source: [AeroRIT: A New Scene for Hyperspectral Image Analysis](/paper/aerorit-a-new-scene-for-hyperspectral-image)
Image Source: [https://github.com/aneesh3108/AeroRIT](https://github.com/aneesh3108/AeroRIT)

Introducing paper: AeroRIT: A New Scene for Hyperspectral Image Analysis
AESLC

To study the task of email subject line generation: automatically generating an email subject line from the email body. 

Source: [This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation](/paper/this-email-could-save-your-life-introducing)

Introducing paper: This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
Aesthetics Text Corpus

An exhaustive list of stop lemmas created from 12 corpora across multiple domains, consisting of over 13 million words, from which more than 200,000 lemmas were generated, and 11 publicly available stop word lists comprising over 1000 words, from which nearly 400 unique lemmas were generated.

Source: [Novel Language Resources for Hindi: An Aesthetics Text Corpus and a Comprehensive Stop Lemma List](/paper/novel-language-resources-for-hindi-an)

Introducing paper: Novel Language Resources for Hindi: An Aesthetics Text Corpus and a Comprehensive Stop Lemma List
Affective Text

Affective Text (Test Corpus of SemEval 2007) by [Carlo Strapparava & Rada Mihalcea](https://www.aclweb.org/anthology/S07-1013/).

Source: [Affective Text](https://github.com/sarnthil/unify-emotion-datasets/tree/master/datasets)
Aff-Wild2

Aff-Wild2 is a large-scale in-the-wild database and an extension of the Aff-Wild dataset for affect recognition. It approximately doubles the number of included video frames and the number of subjects; thus, improving the variability of the included behaviors and of the involved persons. It is the only existing in-the-wild database with annotations for all 3 main behaviour tasks.

The Aff-Wild2 is annotated in a per frame basis for the seven basic expressions (i.e., happiness, surprise, anger, disgust, fear, sadness and the neutral state), twelve action units (AUs 1,2,4,6,7,10,12,15,23,24,25, 26) and valence and arousal. In total Aff-Wild2 consists of 564 videos of around 2.8M frames with 554 subjects.  Aff-Wild2 displays a big diversity in terms of subjects' ages, ethnicities and nationalities; it has also great variations and diversities of environments.



Sources:
1)  [Expression, affect, action unit recognition: Aff-wild2, multi-task learning and arcface](https://arxiv.org/pdf/1910.04855);
2)  [The 6th affective behavior analysis in-the-wild (abaw) competition](https://openaccess.thecvf.com/content/CVPR2024W/ABAW/papers/Kollias_The_6th_Affective_Behavior_Analysis_In-the-wild_ABAW_Competition_CVPRW_2024_paper.pdf)

Introducing paper: Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace
AfroMNIST

A set of synthetic MNIST-style datasets for four orthographies used in Afro-Asiatic and Niger-Congo languages: Ge`ez (Ethiopic), Vai, Osmanya, and N'Ko. These datasets serve as "drop-in" replacements for MNIST. 

Source: [Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languages](/paper/afro-mnist-synthetic-generation-of-mnist)

Introducing paper: Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languages
Agriculture-Vision

A large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. Collects 94,986 high-quality aerial images from 3,432 farmlands across the US, where each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. 

Source: [Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis](/paper/agriculture-vision-a-large-aerial-image)

Introducing paper: Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
AGRR-2019

Consists of 7.5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts. The dataset was prepared for the Automatic Gapping Resolution Shared Task for Russian (AGRR-2019) - a competition aimed at stimulating the development of NLP tools and methods for processing of ellipsis. 

Source: [AGRR-2019: A Corpus for Gapping Resolution in Russian](/paper/agrr-2019-a-corpus-for-gapping-resolution-in)

Introducing paper: AGRR-2019: A Corpus for Gapping Resolution in Russian
AI2D-RST

AI2D-RST is a multimodal corpus of 1000 English-language diagrams that represent topics in primary school natural sciences, such as food webs, life cycles, moon phases and human physiology. The corpus is based on the Allen Institute for Artificial Intelligence Diagrams (AI2D) dataset, a collection of diagrams with crowd-sourced descriptions, which was originally developed to support research on automatic diagram understanding and visual question answering. 

Source: [AI2D-RST: A multimodal corpus of 1000 primary school science diagrams](/paper/ai2d-rst-a-multimodal-corpus-of-1000-primary)

Introducing paper: AI2D-RST: A multimodal corpus of 1000 primary school science diagrams