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 …

One essential problem in skeleton-based action recognition is how to extract discriminative
features over all skeleton joints. However, the complexity of the recent State-Of-The-Art …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

With the advancement of computer vision, human action recognition (HAR) has shown its
broad research worth and application prospects in a wide range of fields such as intelligent …

In recent years, graph convolutional networks (GCNs) play an increasingly critical role in
skeleton-based human action recognition. However, most GCN-based methods still have …

Person re-identification (Re-ID) via gait features within 3D skeleton sequences is a newly-
emerging topic with several advantages. Existing solutions either rely on hand-crafted …

Action recognition based on skeleton data has recently witnessed increasing attention and
progress. State-of-the-art approaches adopting Graph Convolutional networks (GCNs) can …

In the task of skeleton-based action recognition, long-term temporal dependencies are
significant cues for sequential skeleton data. State-of-the-art methods rarely have access to …

In the task of human motion recognition, the overall action span is changeable, and there
may be an inclusion relationship between action semantics. This paper proposes a novel …

With the advances in capturing 2D or 3D skeleton data, skeleton-based action recognition
has received an increasing interest over the last years. As skeleton data is commonly …