23 datasets found

The SHG-VQA model predicts a situation hyper-graph structure composed of existing actions and relations in the input video.

This paper proposes a method for action search in videos, which is used for spotting actions in videos.

The Breakfast dataset is another dataset used in the paper, which contains 712 videos of people performing various activities, such as making coffee or scrambling eggs. The...

AViD is a new public video dataset for action recognition, containing action videos from diverse countries.

The THUMOS'13 dataset contains 24 classes and 3,207 videos.

The JHMDB dataset contains a varying number of actions (21 in JHMDB) across different domains (sports and daily activities).

The UCF Sports dataset contains a varying number of actions (10 in UCF Sports, 21 in JHMDB, and 24 in THUMOS’13) across different domains (sports and daily activities).

Temporal action detection in untrimmed videos via multi-stage cnns, Cdc: convolutional-de-convolutional networks for precise temporal action localization, Temporal action...

The dataset used in the paper to evaluate static bias in action representations.

Generating human action proposals in untrimmed videos is an important yet challenging task with wide applications. Current methods often suffer from the noisy boundary locations...

UCF-24 and JHMDB-21 are two public action datasets used for evaluation of action detection algorithms.

The Kinetics dataset is a large-scale action recognition dataset. It contains videos of various actions performed by humans, with annotations of the actions performed.

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.

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.

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 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...

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...