The task of action detection aims at deducing both the action category and localization of the
start and end moment for each action instance in a long, untrimmed video. While vision …

Temporal action detection aims to predict the time intervals and the classes of action
instances in the video. Despite the promising performance, existing two-stream models …

The recent introduction of the large-scale, long-form MAD and Ego4D datasets has enabled
researchers to investigate the performance of current state-of-the-art methods for video …

Weakly-supervised temporal action localization (WTAL) learns to detect and classify action
instances with only category labels. Most methods widely adopt the off-the-shelf …

It is challenging for artificial intelligence systems to achieve accurate video recognition
under the scenario of low computation costs. Adaptive inference based efficient video …

Efficient video recognition is a hot-spot research topic with the explosive growth of
multimedia data on the Internet and mobile devices. Most existing methods select the salient …

The temporal action localization research aims to discover action instances from untrimmed
videos, representing a fundamental step in the field of intelligent video understanding. With …

To enable progress towards egocentric agents capable of understanding everyday tasks
specified in natural language, we propose a benchmark and a synthetic dataset called …

It is challenging to generate temporal action proposals from untrimmed videos. In general,
boundary-based temporal action proposal generators are based on detecting temporal …

Temporal action proposal generation (TAPG) serves as a promising solution for video
analysis. However, the performance of existing methods is still far from satisfactory for real …