Omkar2118/datasets huggingface dataset:
VideoNavQA

The VideoNavQA dataset contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the Embodied Question Answering (EQA) task.

VideoNavQA contains approximately 101,000 pairs of videos and questions, 28 types of questions belonging to 8 categories, with 70 possible answers. Each question type is
associated with a template that facilitates programmatic generation using ground truth information extracted from the video. The complexity of the questions in the dataset is far beyond that of other similar tasks using this generation method (such as CLEVR): the questions involve single or multiple object/room existence, object/room counting, object color recognition and localization, spatial reasoning, object/room size comparison and equality of object attributes (color, room location).

Source: [VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering](https://arxiv.org/abs/1908.04950)

Introducing paper: VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
OSTD
Full name: Open-Source-Total-Distance

This dataset consists of 18 movies with duration range between 10 and 104 minutes leveraged from the OVSD dataset (Rotman et al., 2016). For these videos, the summary length limit is set to be the minimum between 4 minutes and 10% of the video length.

Source: [ILS-SUMM: ITERATED LOCAL SEARCH FOR UNSUPERVISED VIDEO SUMMARIZATION](https://arxiv.org/pdf/1912.03650.pdf)

Introducing paper: ILS-SUMM: Iterated Local Search for Unsupervised Video Summarization
PolarRR

PolarRR is a new dataset with more than 100 types of glass in which obtained transmission images are perfectly aligned with input mixed images.

Source: [Polarized Reflection Removal with Perfect Alignment in the Wild](https://openaccess.thecvf.com/content_CVPR_2020/papers/Lei_Polarized_Reflection_Removal_With_Perfect_Alignment_in_the_Wild_CVPR_2020_paper.pdf)

Introducing paper: Polarized Reflection Removal with Perfect Alignment in the Wild
Lytro Illum

Lytro Illum is a new light field dataset using a Lytro Illum camera. 640 light fields are collected with significant variations in terms of size, textureness, background clutter and illumination, etc. Micro-lens image arrays and central viewing images are generated, and corresponding ground-truth maps are produced.

Source: [Lytro Illum](https://github.com/pencilzhang/MAC-light-field-saliency-net)

Introducing paper: Light Field Saliency Detection with Deep Convolutional Networks
UFPR-Eyeglasses

The UFPR-Eyeglasses dataset has 1,135 images of both eyes (2,270 cropped images of each eye) from 83 subjects (166 classes). The dataset is used to evaluate the effect of the occlusion caused by eyeglasses in periocular recognition.

Source: [Unconstrained Periocular Recognition: Using Generative Deep Learning Frameworks for Attribute Normalization](https://arxiv.org/abs/2002.03985)

Introducing paper: Unconstrained Periocular Recognition: Using Generative Deep Learning Frameworks for Attribute Normalization
Circa

The Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions.

The dataset contains pairs of yes/no questions and indirect answers, together with annotations for the interpretation of the answer. The data is collected in 10 different social conversational situations (eg. food preferences of a friend). Examples:

```
Q: Are you vegan?
A: I love burgers too much. [No]

Q: Do you like spicy food?
A: I put hot sauce on everything. [Yes] 

Q: Would you like to go see live music?
A: If it’s not too crowded. [Yes, upon a condition]
```

Currently, the Circa annotations focus on a few classes such as ‘yes’, ‘no’ and ‘yes, upon condition’. The data can be used to build machine learning models which can replicate these classes on new question-answer pairs, and allow evaluation of methods for doing so.

Source: [Circa](https://github.com/google-research-datasets/circa)

Introducing paper: "I'd rather just go to bed": Understanding Indirect Answers
QUVA Repetition

QUVA Repetition dataset consists of 100 videos displaying a wide variety of repetitive video dynamics, including swimming, stirring, cutting, combing and music-making. All videos have been annotated with individual cycle bounds and a total repetition count.

Source: [Real-world Repetition Estimation by Div, Grad and Curl](https://tomrunia.github.io/projects/repetition/)

Introducing paper: Real-World Repetition Estimation by Div, Grad and Curl
NH-HAZE

NN-HAZE is an image dehazing dataset. Since in many real cases haze is not uniformly distributed NH-HAZE, a non-homogeneous realistic dataset with pairs of real hazy and corresponding haze-free images. This is the first non-homogeneous image dehazing dataset and contains 55 outdoor scenes. The non-homogeneous haze has been introduced in the scene using a professional haze generator that imitates the real conditions of hazy scenes.

Source: [NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images](https://data.vision.ee.ethz.ch/cvl/ntire20/nh-haze/)

Introducing paper: NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images
PVDN
Full name: Provident Vehicle Detection at Night

PVDN is a dataset of vehicle detection at night, using light reflections caused by their headlamps. It contains 59,746 annotated grayscale images out of 346 different scenes in a rural environment at night. In these images, all oncoming vehicles, their corresponding light objects (e. g., headlamps), and their respective light reflections (e. g., light reflections on guardrails) are labeled. With this information, this dataset enables research into new methods of detecting oncoming vehicles based on the light reflections they cause, long before they are directly visible. 

Source: [Provident Vehicle Detection at Night: The PVDN Dataset](https://arxiv.org/abs/2012.15376)

Introducing paper: Provident Vehicle Detection at Night: The PVDN Dataset
JEC-QA

JEC-QA is a LQA (Legal Question Answering) dataset collected from the National Judicial Examination of China. It contains 26,365 multiple-choice and multiple-answer questions in total. The task of the dataset is to predict the answer using the questions and relevant articles. To do well on JEC-QA, both retrieving and answering are important.

Source: [JEC-QA Website](https://jecqa.thunlp.org/)

Introducing paper: JEC-QA: A Legal-Domain Question Answering Dataset
TrashCan

The TrashCan dataset is an instance-segmentation dataset of underwater trash. It is comprised of annotated images (7,212 images) which contain observations of trash, ROVs, and a wide variety of undersea flora and fauna. The annotations in this dataset take the format of instance segmentation annotations: bitmaps containing a mask marking which pixels in the image contain each object. The imagery in TrashCan is sourced from the J-EDI (JAMSTEC E-Library of Deep-sea Images) dataset, curated by the Japan Agency of Marine Earth Science and Technology (JAMSTEC). 

Source: [TrashCan 1.0 An Instance-Segmentation Labeled Dataset of Trash Observations](https://conservancy.umn.edu/handle/11299/214865)
Image Source: [https://conservancy.umn.edu/handle/11299/214865](https://conservancy.umn.edu/handle/11299/214865)

Introducing paper: TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris
UCLA Aerial Event Dataset

The UCLA Aerial Event Dataest has been captured by a low-cost hex-rotor with a GoPro camera, which is able to eliminate the high frequency vibration of the camera and hold in air autonomously through a GPS and a barometer. It can also fly 20 ∼ 90m above the ground and stays 5 minutes in air. 

This hex-rotor has been used to take the set of videos in the dataset, captured in different places: hiking routes, parking lots, camping sites, picnic areas with shelters, restrooms, tables, trash bins and BBQ ovens. By detecting/tracking humans and objects in the videos, the videos can be annotated with events.

The original videos are pre-processed, including camera calibration and frame registration. After pre-processing, there are totally 27 videos in the dataset, the length of which ranges from 2 minutes to 5 minutes. Each video has annotations with hierarchical semantic information of objects, roles, events and groups in the videos.

Source: [Joint Inference of Groups, Events and Human Roles in Aerial Videos](http://www.stat.ucla.edu/~tianmin.shu/AerialVideo/AerialVideo.html)

Introducing paper: Joint Inference of Groups, Events and Human Roles in Aerial Videos
CQASUMM

CQASUMM is a dataset for CQA (Community Question Answering) summarization, constructed from the 4.4 million Yahoo! Answers L6 dataset. The dataset contains ~300k annotated samples.

Source: [CQASUMM](https://bitbucket.org/tanya14109/cqasumm/src/master/)

Introducing paper: CQASUMM: Building References for Community Question Answering Summarization Corpora
NeuralNews

NeuralNews is a dataset for machine-generated news detection. It consists of human-generated and machine-generated articles. The human-generated articles are extracted from the GoodNews dataset, which is extracted from the New York Times. It contains 4 types of articles:

- Real Articles and Real Captions
- Real Articles and Generated Captions
- Generated Articles and Real Captions
- Generated Articles and Generated Captions

In total, it contains about 32K samples of each article type (resulting in about 128K total).

Source: [Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News](https://arxiv.org/abs/2009.07698)

Introducing paper: Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News
EyeCar

EyeCar is a dataset of driving videos of vehicles involved in rear-end collisions paired with eye fixation data captured from human subjects. It contains 21 front-view videos that were captured in various traffic, weather, and day light conditions. Each video is 30sec in length and contains typical driving tasks (e.g., lanekeeping, merging-in, and braking) ending to rear-end collisions.

Source: [MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning](https://arxiv.org/abs/1912.07773)

Introducing paper: MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning
WordNet-feelings

WordNet-feelings, is an affective dataset that identifies 3664 word senses as feelings, and associates each of these with one of the 9 categories of feeling. The 9 different categories are: Actions, Anger, Attention, Attraction, Hedonics, Other, Physiological, Social, Wellbeing.

Source: [WordNet-feelings: A linguistic categorisation of human feelings](https://arxiv.org/abs/1811.02435)

Introducing paper: WordNet-feelings: A linguistic categorisation of human feelings
Doc3DShade

Doc3DShade extends Doc3D with realistic lighting and shading. Follows a similar synthetic rendering procedure using captured document 3D shapes but final image generation step combines real shading of different types of paper materials under numerous illumination conditions.

Source: [Doc3DShade](https://github.com/cvlab-stonybrook/DocIIW)

Introducing paper: Intrinsic Decomposition of Document Images In-the-Wild
Deep Fakes Dataset
Full name: inamibora

The Deep Fakes Dataset is a collection of "in the wild" portrait videos for deepfake detection. The videos in the dataset are diverse real-world samples in terms of the source generative model, resolution, compression, illumination, aspect-ratio, frame rate, motion, pose, cosmetics, occlusion, content, and context. They originate from various sources such as news articles, forums, apps, and research presentations; totalling up to 142 videos, 32 minutes, and 17 GBs. Synthetic videos are matched with their original counterparts when possible. 

Source: [Deepfakes dataset](http://cs.binghamton.edu/~ncilsal2/DeepFakesDataset/)

Introducing paper: FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
SketchyScene

SketchyScene is a large-scale dataset of scene sketches to advance research on sketch understanding at both the object and scene level. The dataset is created through a novel and carefully designed crowdsourcing pipeline, enabling users to efficiently generate large quantities of realistic and diverse scene sketches. SketchyScene contains more than 29,000 scene-level sketches, 7,000+ pairs of scene templates and photos, and 11,000+ object sketches. All objects in the scene sketches have ground-truth semantic and instance masks. The dataset is also highly scalable and extensible, easily allowing augmenting and/or changing scene composition. 

Source: [SketchyScene: Richly-Annotated Scene Sketches](https://openaccess.thecvf.com/content_ECCV_2018/papers/Changqing_Zou_SketchyScene_Richly-Annotated_Scene_ECCV_2018_paper.pdf)
Image Source: [Zoe et al]( SketchyScene: Richly-Annotated Scene Sketches)

Introducing paper: SketchyScene: Richly-Annotated Scene Sketches
ECHR

ECHR is an English legal judgment prediction dataset of cases from the European Court of Human Rights (ECHR). The dataset contains ~11.5k cases, including the raw text.

For each case, the dataset provides a list of facts extracted using regular expressions from the case description. Each case is also mapped to articles of the Convention that were violated (if any). An importance score is also assigned by ECHR.

Source: [Neural Legal Judgment Prediction in English](https://arxiv.org/abs/1906.02059)

Introducing paper: Neural Legal Judgment Prediction in English
AmazonQA

AmazonQA consists of 923k questions, 3.6M answers and 14M reviews across 156k products. Building on the well-known Amazon dataset, additional annotations are collected, marking each question as either answerable or unanswerable based on the available reviews. 

Source: [AmazonQA: A Review-Based Question Answering Task](https://arxiv.org/pdf/1908.04364.pdf)

Introducing paper: AmazonQA: A Review-Based Question Answering Task
TurkQA

TurkQA consists of a selection of sentences from English Wikipedia articles, with questions and answers crowdsourced from workers on Amazon Mechanical Turk.

Source: [TurkQA](https://groupring.net/malon/turkqa/index.html)

Introducing paper: Answer Extraction by Recursive Parse Tree Descent
XTREME
Full name: Cross-Lingual Transfer Evaluation of Multilingual Encoders

The **Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME)** benchmark was introduced to encourage more research on multilingual transfer learning,. XTREME covers 40 typologically diverse languages spanning 12 language families and includes 9 tasks that require reasoning about different levels of syntax or semantics.

The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil (spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the Niger-Congo languages Swahili and Yoruba, spoken in Africa.

Introducing paper: XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation
MDD
Full name: Movie Dialog dataset

Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion).

Source: [Movie Dialog dataset](https://research.fb.com/downloads/babi/)

Introducing paper: Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems