Human activity recognition is essential in many domains, including the medical and smart
home sectors. Using deep learning, we conduct a comprehensive survey of current state …

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 …

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 …

Skeleton-based action segmentation requires recognizing composable actions in untrimmed
videos. Current approaches decouple this problem by first extracting local visual features …

Supervised learning of skeleton sequence encoders for action recognition has received
significant attention in recent times. However, learning such encoders without labels …

In the field of skeleton-based action recognition, current top-performing graph convolutional
networks (GCNs) exploit intra-sequence context to construct adaptive graphs for feature …

Unsupervised pre-training has shown great success in skeleton-based action understanding
recently. Existing works typically train separate modality-specific models (ie, joint, bone, and …

Self-supervised representation learning has proven constructive for skeleton-based action
recognition. For better performance, existing methods mainly focus on 1) multi-modal data …

Self-supervised learning has proved effective for skeleton-based human action
understanding, which is an important yet challenging topic. Previous works mainly rely on …