Skeleton-based action recognition has rapidly become one of the most popular and
essential research topics in computer vision. The task is to analyze the characteristics of …

Action recognition is a major branch of computer vision research. As a widely used
technology, action recognition has been applied to human–computer interaction, intelligent …

In action recognition, although the combination of spatio-temporal videos and skeleton
features can improve the recognition performance, a separate model and balancing feature …

Skeleton sequence representation learning has shown great advantages for action
recognition due to its promising ability to model human joints and topology. However, the …

In 3D human action recognition, limited supervised data makes it challenging to fully tap into
the modeling potential of powerful networks such as transformers. As a result, researchers …

The self-supervised pretraining paradigm has achieved great success in skeleton-based
action recognition. However, these methods treat the motion and static parts equally, and …

Increasing scene-awareness is a key challenge in video anomaly detection (VAD). In this
work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware …

Contrastive learning has been successfully leveraged to learn action representations for
addressing the problem of semisupervised skeleton-based action recognition. However …

Most semi-supervised skeleton-based action recognition approaches aim to learn the
skeleton action representations only at the joint level, but neglect the crucial motion …

Contrastive learning has been proven beneficial for self-supervised skeleton-based action
recognition. Most contrastive learning methods utilize carefully designed augmentations to …