Omkar2118/datasets huggingface dataset:
VidSet

A large video dataset with dynamic content.

Source: [Deep Homography Estimation for Dynamic Scenes](/paper/deep-homography-estimation-for-dynamic-scenes)

Introducing paper: Deep Homography Estimation for Dynamic Scenes
VidSTG

The **VidSTG** dataset is a spatio-temporal video grounding dataset constructed based on the video relation dataset VidOR. VidOR contains 7,000, 835 and 2,165 videos for training, validation and testing, respectively. The goal of the Spatio-Temporal Video Grounding task (STVG) is to localize the spatio-temporal section of an untrimmed video that matches a given sentence depicting an object. **VidSTG** contains 5,563, 618, and 743 videos for training, validation, and testing, respectively. 

Source: [https://github.com/Guaranteer/VidSTG-Dataset](https://github.com/Guaranteer/VidSTG-Dataset)
Image Source: [https://github.com/Guaranteer/VidSTG-Dataset](https://github.com/Guaranteer/VidSTG-Dataset)

Introducing paper: Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences
VIENA2

Covers 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class. 

Source: [VIENA2: A Driving Anticipation Dataset](/paper/viena2-a-driving-anticipation-dataset)

Introducing paper: VIENA2: A Driving Anticipation Dataset
ViMMRC
Full name: Vietnamese Multiple-choice Machine Reading Comprehension Corpus

A challenging machine comprehension corpus with multiple-choice questions, intended for research on the machine comprehension of Vietnamese text. This corpus includes 2,783 multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders. Answers may be extracted from the contents of single or multiple sentences in the corresponding reading text.

Source: [ViMMRC](https://sites.google.com/uit.edu.vn/uit-nlp/datasets-projects)

Introducing paper: Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension
Violin
Full name: VIdeO-and-Language INference

Video-and-Language Inference is the task of joint multimodal understanding of video and text. Given a video clip with aligned subtitles as premise, paired with a natural language hypothesis based on the video content, a model needs to infer whether the hypothesis is entailed or contradicted by the given video clip. The **Violin** dataset is a dataset for this task which consists of 95,322 video-hypothesis pairs from 15,887 video clips, spanning over 582 hours of video. These video clips contain rich content with diverse temporal dynamics, event shifts, and people interactions, collected from two sources: (i) popular TV shows, and (ii) movie clips from YouTube channels.

Source: [https://github.com/jimmy646/violin](https://github.com/jimmy646/violin)
Image Source: [https://github.com/jimmy646/violin](https://github.com/jimmy646/violin)

Introducing paper: VIOLIN: A Large-Scale Dataset for Video-and-Language Inference
VIPL-HR

VIPL-HR database is a database for remote heart rate (HR) estimation from face videos under less-constrained situations. It contains 2,378 visible light videos (VIS) and 752 near-infrared (NIR) videos of 107 subjects. Nine different conditions, including various head movements and illumination conditions are taken into consideration. All the videos are recorded using Logitech C310, RealSense F200 and the front camera of HUAWEI P9 smartphone, and the ground-truth HR is recorded using a CONTEC CMS60C BVP sensor (a FDA approved device). 

Source: [VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video](/paper/vipl-hr-a-multi-modal-database-for-pulse)

Introducing paper: VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video
Virtual Gallery

The Virtual Gallery dataset is a synthetic dataset that targets multiple challenges such as varying lighting conditions and different occlusion levels for various tasks such as depth estimation, instance segmentation and visual localization.

It consists of a scene containing 3-4 rooms, in which a total of 42 free-for-use famous paintings are placed on the walls.

The virtual model and the captured images were generated with Unity software, allowing us to extract ground-truth information such as depth, semantic and instance segmentation, 2D-2D and 2D-3D correspondences.

Source: [Visual Localization by Learning Objects-Of-Interest Dense Match Regression](https://europe.naverlabs.com/research/3d-vision/virtual-gallery-dataset/)
Image Source: [https://europe.naverlabs.com/research/3d-vision/virtual-gallery-dataset/](https://europe.naverlabs.com/research/3d-vision/virtual-gallery-dataset/)

Introducing paper: Visual Localization by Learning Objects-Of-Interest Dense Match Regression
VisDrone

**VisDrone** is a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, to make vision meet drones. The VisDrone2019 dataset is collected by the AISKYEYE team at Lab of Machine Learning and Data Mining, Tianjin University, China. The benchmark dataset consists of 288 video clips formed by 261,908 frames and 10,209 static images, captured by various drone-mounted cameras, covering a wide range of aspects including location (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and country), objects (pedestrian, vehicles, bicycles, etc.), and density (sparse and crowded scenes). Note that, the dataset was collected using various drone platforms (i.e., drones with different models), in different scenarios, and under various weather and lighting conditions. These frames are manually annotated with more than 2.6 million bounding boxes of targets of frequent interests, such as pedestrians, cars, bicycles, and tricycles. Some important attributes including scene visibility, object class and occlusion, are also provided for better data utilization.

Source: [https://github.com/VisDrone/VisDrone-Dataset](https://github.com/VisDrone/VisDrone-Dataset)
Image Source: [https://arxiv.org/pdf/2001.06303.pdf](https://arxiv.org/pdf/2001.06303.pdf)

Introducing paper: Vision Meets Drones: A Challenge
Vistas-NP

The **Vistas-NP** dataset is an out-of-distribution detection dataset based on the Mapillary Vistas dataset. The original Vistas dataset consists of 18,000 training images and 2,000 validation images with 66 classes. In Vistas-NP the human classes are used as outliers due to their dispersion across scenes and visual diversity from other objects. The dataset is created by excluding all images with class person and the three rider classes to the test subset. Consequently, the dataset has 8,003 train images and 830 validation images. The test set contains 11,167.

Source: [https://github.com/matejgrcic/Vistas-NP](https://github.com/matejgrcic/Vistas-NP)

Introducing paper: Dense open-set recognition with synthetic outliers generated by Real NVP