Omkar2118/datasets huggingface dataset:
HowToVQA69M
Full name: HowToVQA69M

A dataset of 69,270,581 video clip, question and answer triplets (v, q, a). HowToVQA69M is two orders of magnitude larger than any of the currently available VideoQA datasets.

On average, each original video results in 43 video clips, where each clip lasts 12.1 seconds and is associated to 1.2 question-answer pairs. Questions and answers contain 8.7 and 2.4 words on average respectively. HowToVQA69M is highly diverse and contains over 16M unique answers, where over 2M unique answers appear more than once and over 300K unique answers appear more than ten times.

Source: [Just Ask: Learning to Answer Questions from Millions of Narrated Videos](https://arxiv.org/pdf/2012.00451.pdf)

Introducing paper: Just Ask: Learning to Answer Questions from Millions of Narrated Videos
Yesno
Full name: Yesno

**Yesno** is an audio dataset consisting of 60 recordings of one individual saying yes or no in Hebrew; each recording is eight words long. It was created for the Kaldi audio project by an author who wishes to remain anonymous.
BCCD
Full name: BCCD

**BCCD** is a small-scale dataset for blood cells detection.
M-VAD Names
Full name: M-VAD Names Dataset

The dataset contains the annotations of characters' visual appearances, in the form of tracks of face bounding boxes, and the associations with characters' textual mentions, when available. The detection and annotation of the visual appearances of characters in each video clip of each movie was achieved through a semi-automatic approach. The released dataset contains more than 24k annotated video clips, including 63k visual tracks and 34k textual mentions, all associated with their character identities.

Source: [M-VAD Names Dataset](https://github.com/aimagelab/mvad-names-dataset)

Introducing paper: M-VAD Names: a Dataset for Video Captioning with Naming
TutorialVQA
Full name: TutorialVQA

**TutorialVQA** is a new type of dataset used to find answer spans in tutorial videos. The dataset includes about 6,000 triples, comprised of videos, questions, and answer spans manually collected from screencast tutorial videos with spoken narratives for a photo-editing software.

Source: [TutorialVQA: Question Answering Dataset for Tutorial Videos](https://arxiv.org/pdf/1912.01046v2.pdf)

Introducing paper: TutorialVQA: Question Answering Dataset for Tutorial Videos
How2R
Full name: How2R

Amazon Mechanical Turk (AMT) is used to collect annotations on HowTo100M videos. 30k 60-second clips are randomly sampled from 9,421 videos and present each clip to the turkers, who are asked to select a video segment containing a single, self-contained scene. After this segment selection step, another group of workers are asked to write descriptions for each displayed segment. Narrations are not provided to the workers to ensure that their written queries are based on visual content only. These final video segments are 10-20 seconds long on average, and the length of queries ranges from 8 to 20 words. From this process, 51,390 queries are collected for 24k 60-second clips from 9,371 videos in HowTo100M, on average 2-3 queries per clip. The video clips and its associated queries are split into 80% train, 10% val and 10% test.

Source: [HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://arxiv.org/pdf/2005.00200v2.pdf)

Introducing paper: HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
How2QA
Full name: How2QA

To collect How2QA for video QA task, the same set of selected video clips are presented to another group of AMT workers for multichoice QA annotation. Each worker is assigned with one video segment and asked to write one question with four answer candidates (one correctand three distractors). Similarly, narrations are hidden from the workers to ensure the collected QA pairs are not biased by subtitles. Similar to TVQA, the start and end points are provided for the relevant moment for each question. After filtering low-quality annotations, the final dataset contains 44,007 QA pairs for 22k 60-second clips selected from 9035 videos.

Source: [HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://arxiv.org/pdf/2005.00200v2.pdf)

Introducing paper: HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
CLIC
Full name: Challenge on Learned Image Compression

**CLIC** is a dataset for learned image compression. The dataset contains both RGB and grayscale images.
CoNLL

The CoNLL dataset is a widely used resource in the field of natural language processing (NLP). The term “CoNLL” stands for Conference on Natural Language Learning. It originates from a series of shared tasks organized at the Conferences of Natural Language Learning.
PyBullet

PyBullet is an easy to use Python module for physics simulation, robotics and deep reinforcement learning based on the Bullet Physics SDK. With PyBullet you can load articulated bodies from URDF, SDF and other file formats. PyBullet provides forward dynamics simulation, inverse dynamics computation, forward and inverse kinematics and collision detection and ray intersection queries. Aside from physics simulation, PyBullet supports to rendering, with a CPU renderer and OpenGL visualization and support for virtual reality headsets.
Vid4

The Vid4 dataset is generally used for testing video super-resolution. It consists of four sequences: walk (740x480, 47 frames), foliage (740x480, 49 frames), city (704x576, 34 frames), and calendar (720x576, 41 frames).
PixelHelp
Full name: PixelHelp

PixelHelp includes 187 multi-step instructions of 4 task categories deined in https://support.google.com/pixelphone and annotated by human. This dataset includes 88 general tasks, such as configuring accounts, 38 Gmail tasks, 31 Chrome tasks, and 30 Photos related tasks. This dataset is an updated opensource version of the original PixelHelp dataset, which was used for testing the end-to-end grounding quality of the model in paper "Mapping Natural Language Instructions to Mobile UI Action Sequences". The similar accuracy is acquired on this version of the dataset.

Source: [PixelHelp](https://github.com/google-research-datasets/seq2act/tree/master/data/pixel_help)

Introducing paper: Mapping Natural Language Instructions to Mobile UI Action Sequences
RicoSCA
Full name: RicoSCA

Rico is a public UI corpus with 72K Android UI screens mined from 9.7K Android apps (Deka et al., 2017). Each screen in Rico comes with a screenshot image and a view hierarchy of a collection of UI objects. Authors manually removed screens whose view hierarchies do not match their screenshots by asking annotators to visually verify whether the bounding boxes of view hierarchy leaves match each UI object on the corresponding screenshot image. This filtering results in 25K unique screens.

In total, RICOSCA contains 295,476 single-step synthetic commands for operating 177,962 different target objects across 25,677 Android screens.

Source: [Google Research](https://github.com/google-research/google-research/blob/master/seq2act/data_generation/README.md)

Introducing paper: Mapping Natural Language Instructions to Mobile UI Action Sequences
AndroidHowTo
Full name: AndroidHowTo

AndroidHowTo contains 32,436 data points from 9,893 unique How-To instructions and split into training (8K), validation (1K) and test (900). All test examples have perfect agreement across all three annotators for the entire sequence. In total, there are 190K operation spans, 172K object spans, and 321 input spans labeled. The lengths of the instructions range from 19 to 85 tokens, with median of 59. They describe a sequence of actions from one to 19 steps, with a median of 5.

Source: [Google Research](https://github.com/google-research/google-research/blob/master/seq2act/data_generation/README.md)

Introducing paper: Mapping Natural Language Instructions to Mobile UI Action Sequences
MIND
Full name: MIcrosoft News Dataset

MIcrosoft News Dataset (MIND) is a large-scale dataset for news recommendation research. It was collected from anonymized behavior logs of Microsoft News website. The mission of MIND is to serve as a benchmark dataset for news recommendation and facilitate the research in news recommendation and recommender systems area.

MIND contains about 160k English news articles and more than 15 million impression logs generated by 1 million users. Every news article contains rich textual content including title, abstract, body, category and entities. Each impression log contains the click events, non-clicked events and historical news click behaviors of this user before this impression. To protect user privacy, each user was de-linked from the production system when securely hashed into an anonymized ID.

Source: [MIND](https://msnews.github.io/)

Introducing paper: MIND: A Large-scale Dataset for News Recommendation