132 datasets found

Propose an action recognition framework using Generative Adversarial Networks. Our model involves training a deep convolutional generative adversarial network (DCGAN) using a...

UCF-101 dataset is a large-scale action recognition dataset, containing 13,320 videos categorized into 101 human action categories.

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

A dataset of 42 animal video clips with diverse species and insufficient view coverage from the Internet.

The DAVIS dataset is a widely used dataset for video-related tasks, consisting of approximately 2000 frames from 26 human-centric scenarios.

The MUSES dataset is a collection of 3,697 videos, with 2,587 for training and 1,110 for testing.

Temporal action localization (TAL) is a prevailing task due to its great application potential. Existing works in this field mainly suffer from two weaknesses: (1) They often...

Temporal action localization (TAL) is a challenging task in video understanding that aims to identify and localize actions within a video sequence.

The DiDeMo dataset is a large-scale video-text dataset, containing 10,000 videos and 40,000 annotations.

The dataset used in the paper is a video question answering dataset, which is a large-scale video-language pre-training task.

The COIN dataset is a large-scale instructional video dataset that contains 100 hours of video. The dataset is used for instructional video analysis and understanding.

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

The Kinetics dataset is a large-scale video dataset for human action recognition.

FineGym is a hierarchical video dataset for fine-grained action understanding, containing 354 action categories.

The dataset used in the paper is the ActivityNet Challenge 2016 dataset.

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 proposed Temporal Correlation Module (TCM) to deal with the variation of action visual tempo in videos, which includes a Multi-scale Temporal Dynamics Module (MTDM) and a...

The Avenue data set consists of 16 training videos with a total 15328 frames and 21 test videos with a total of 15324.

A video dataset for frame duplication detection and localization in forged videos

The ActivityNet Captions is a benchmark dataset proposed for dense video captioning. There are 20K untrimmed videos in total, and each video has several annotated segments with...