Omkar2118/datasets huggingface dataset:
DeeperForensics-1.0
Full name: DeeperForensics-1.0

**DeeperForensics-1.0** represents the largest face forgery detection dataset by far, with 60,000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind. The full dataset includes 48,475 source videos and 11,000 manipulated videos. The source videos are collected on 100 paid and consented actors from 26 countries, and the manipulated videos are generated by a newly proposed many-to-many end-to-end face swapping method, DF-VAE. 7 types of real-world perturbations at 5 intensity levels are employed to ensure a larger scale and higher diversity.
Image Source: [https://github.com/EndlessSora/DeeperForensics-1.0](https://github.com/EndlessSora/DeeperForensics-1.0)

Introducing paper: DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
WikiSuggest

To collect WikiSuggest, Google Suggest API is used to harvest natural language questions and submit them to Google Search. Whenever Google Search returns a box with a short answer from Wikipedia, an example from the question, answer, and the Wikipedia document are created. If the answer string is missing from the document this often implies a spurious question-answer pair, such as (‘what time is half time in rugby’, ‘80 minutes, 40 minutes’). Question-answer pairs without the exact answer string are pruned. Fifty examples after filtering are examined and 54% were found to be well-formed question-answer pairs where answers in the document can be grounded, 20% contained answers without textual evidence in the document (the answer string exists in an irreleveant context), and 26% contain incorrect QA pairs.

Source: [Coarse-to-Fine Question Answering for Long Documents](https://arxiv.org/pdf/1611.01839.pdf)

Introducing paper: Coarse-to-Fine Question Answering for Long Documents
Shmoop Corpus

Shmoop Corpus is a dataset of 231 stories that are paired with detailed multi-paragraph summaries for each individual chapter (7,234 chapters), where the summary is chronologically aligned with respect to the story chapter. From the corpus, a set of common NLP tasks are constructed, including Cloze-form question answering and a simplified form of abstractive summarization, as benchmarks for reading comprehension on stories.

Source: [Shmoop Corpus](http://www.cs.toronto.edu/~makarand/shmoop/)

Introducing paper: The Shmoop Corpus: A Dataset of Stories with Loosely Aligned Summaries
MovieNet
Full name: MovieNet

**MovieNet** is a holistic dataset for movie understanding. MovieNet contains 1,100 movies with a large amount of multi-modal data, e.g. trailers, photos, plot descriptions, etc.. Besides, different aspects of manual annotations are provided in MovieNet, including 1.1M characters with bounding boxes and identities, 42K scene boundaries, 2.5K aligned description sentences, 65K tags of place and action, and 92 K tags of cinematic style.

Introducing paper: MovieNet: A Holistic Dataset for Movie Understanding
MessyTable
Full name: MessyTable

**MessyTable** features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association.

There are 50,211 images and 5,579 scenes in the dataset.

Introducing paper: MessyTable: Instance Association in Multiple Camera Views
TweetQA

With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effectiveness of many applications that rely on real-time knowledge. While previous question answering (QA) datasets have concentrated on formal text like news and Wikipedia, the first large-scale dataset for QA over social media data is presented. To make sure the tweets are meaningful and contain interesting information, tweets used by journalists to write news articles are gathered. Then human annotators are asked to write questions and answers upon these tweets. Unlike other QA datasets like SQuAD in which the answers are extractive, the answer are allowed to be abstractive. The task requires model to read a short tweet and a question and outputs a text phrase (does not need to be in the tweet) as the answer.

Source: [TWEETQA: A Social Media Focused Question Answering Dataset](https://tweetqa.github.io/)

Introducing paper: TWEETQA: A Social Media Focused Question Answering Dataset
UCF Sports

The UCF Sports dataset consists of a set of actions collected from various sports which are typically featured on broadcast television channels such as the BBC and ESPN. The video sequences were obtained from a wide range of stock footage websites including BBC Motion gallery and GettyImages.

The dataset includes a total of 150 sequences with the resolution of 720 x 480. The collection represents a natural pool of actions featured in a wide range of scenes and viewpoints.

Source: [UCF Sports Action](https://www.crcv.ucf.edu/data/UCF_Sports_Action.php)
MSRA-B

The MSRA-B dataset is a dataset for salient object detection. It contains 5,000 images with a variety of image contents. Most of the images have a single salient object. There is a large variation among images including natural scenes, animals, indoor, outdoor, etc.

Source: [Deep Contrast Learning for Salient Object Detection](https://arxiv.org/pdf/1603.01976.pdf)
VOT2015
Full name: Visual Object Tracking Challenge 2015

VOT2015 is a visual object tracking dataset. The dataset comprises 60 short sequences showing various objects in challenging backgrounds. The sequences were chosen from a large pool of sequences from different sources.

Source: [VOT2015](https://www.votchallenge.net/vot2015/dataset.html)
UCF50

UCF50 is an action recognition data set with 50 action categories, consisting of realistic videos taken from youtube. This data set is an extension of YouTube Action data set (UCF11) which has 11 action categories.

UCF50 data set's 50 action categories collected from youtube are: Baseball Pitch, Basketball Shooting, Bench Press, Biking, Biking, Billiards Shot,Breaststroke, Clean and Jerk, Diving, Drumming, Fencing, Golf Swing, Playing Guitar, High Jump, Horse Race, Horse Riding, Hula Hoop, Javelin Throw, Juggling Balls, Jump Rope, Jumping Jack, Kayaking, Lunges, Military Parade, Mixing Batter, Nun chucks, Playing Piano, Pizza Tossing, Pole Vault, Pommel Horse, Pull Ups, Punch, Push Ups, Rock Climbing Indoor, Rope Climbing, Rowing, Salsa Spins, Skate Boarding, Skiing, Skijet, Soccer Juggling, Swing, Playing Tabla, TaiChi, Tennis Swing, Trampoline Jumping, Playing Violin, Volleyball Spiking, Walking with a dog, and Yo Yo.


Source: [UCF50](https://www.crcv.ucf.edu/data/UCF50.php)
MSD
Full name: Million Song Dataset

The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks.

The core of the dataset is the feature analysis and metadata for one million songs, provided by The Echo Nest. The dataset does not include any audio, only the derived features. Note, however, that sample audio can be fetched from services like 7digital, using [code]( https://github.com/tbertinmahieux/MSongsDB/tree/master/Tasks_Demos/Preview7digital) provided by the authors.


Source: [http://millionsongdataset.com/](http://millionsongdataset.com/)
Paper: [The Million Song Dataset](https://doi.org/10.7916/D8NZ8J07)
CASME II
Full name: Chinese Academy of Sciences Micro-Expression II

The Chinese Academy of Sciences Micro-Expression dataset (CASME II) consists of 255 videos, elicited from 26 participants. The videos are recorded using Point Gray GRAS-03K2C camera which has a frame rate of 200fps. The average video length is 0.34s, equivalent to 68 frames. Each video’s emotion label is annotated by two coders, where the reliability is 0.846. 

All the images are cropped to 170×140 pixels. The ground-truth information provided by the database include the emotion state, the action unit, the onset, apex and offset frame indices. The videos are grouped into seven categories: others (99 videos), disgust (63 videos), happiness (32 videos), repression (27 videos), surprise (25 videos), sadness (7 videos) and fear (2 videos).

Source: [OFF-ApexNet on Micro-expression Recognition System](https://arxiv.org/pdf/1805.08699.pdf)
VOT2013
Full name: Visual Object Tracking Challenge 2013

The dataset comprises 16 short sequences showing various objects in challenging backgrounds. The sequences were chosen from a large pool of sequences using a methodology based on clustering visual features of object and background so that those 16 sequences sample evenly well the existing pool. The sequences were annotated by the VOT committee using axis-aligned bounding boxes.


Source: [VOT2013](https://www.votchallenge.net/vot2013/dataset.html)