25 datasets found

The VideoLT dataset contains 1,004 classes and about 256,218 untrimmed videos collected from YouTube, covering a wide range of human activities, including everyday life,...

LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything

The UCF101 and HMDB51 datasets are used for video recognition. The UCF101 dataset contains 101 action categories, while the HMDB51 dataset contains 51 classes.

The dataset used in the paper is a video recognition benchmark, specifically Kinetics-400, Something-Something V2, Epic-Kitchens-100, HMDB51, and UCF101.

The Diving48 dataset is a fine-grained video dataset of competitive diving. It has ∼18k trimmed video clips of 48 unambiguous dive sequences standardized by the professional....

The Mini-Kinetics dataset is a mini version of the Kinetics-400 dataset, containing 240k training samples and 20k validation samples in 400 human action classes.

The dataset used in the LORD framework for autonomous driving, consisting of images, videos, and text-based observations.

The Moments in Time dataset is a large-scale video action recognition dataset.

Mobile Video Networks (MoViNets) is a family of computation and memory efficient video networks that can operate on streaming video for online inference.

Moving MNIST is a benchmark data set for video recognition. There are 10,000 samples including 8,000 for training and 2,000 for test. Each sample consists of 20 sequential gray...

The Jester dataset is of continuous jokes ratings from -10 to 10, containing the jokes’ texts.

Video classification is a fundamental problem in many video-based tasks. Applications such as autonomous driving technology, controlling drones and robots are driving the demand...

Video recognition aims to automatically analyze the contents of videos (e.g., events and actions), and has a wide range of applications, including intelligent surveillance,...

The Kinetics-600 dataset consists of 392k training videos and 30k validation videos in 600 human action categories.

The proposed multi-fiber architecture is used for reducing the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while...

Motion has shown to be useful for video understanding, where motion is typically represented by optical flow. However, computing flow from video frames is very time-consuming....

The Something-Something V1 & V2 dataset is a large-scale video dataset created by crowdsourcing. It contains about 100k videos over 174 categories, and the number of videos...

The UCF101 dataset contains 13320 videos distributed in 101 action categories. This dataset is different from the above ones in that it contains mostly coarse sports activities...

Kinetics-700 is a large-scale video dataset for human action recognition, with 700 action categories.

The dataset used for video action classification, consisting of 9.8k training videos, 1.8k validation videos, and 157 classes.