Video-based human action recognition is one of the most important and challenging areas
of research in the field of computer vision. Human action recognition has found many …

Deep neural networks based purely on attention have been successful across several
domains, relying on minimal architectural priors from the designer. In Human Action …

Graph representation learning has re-emerged as a fascinating research topic due to the
successful application of graph convolutional networks (GCNs) for graphs and inspires …

Modeling spatiotemporal global dependencies and dynamics of body joints are crucial to
recognizing actions from 3D skeleton sequences. We propose a Relation-mining Self …

Capturing the dependencies between joints is critical in skeleton-based action recognition
task. Transformer shows great potential to model the correlation of important joints …

Graph convolutional networks (GCNs) are widely adopted in skeleton-based action
recognition due to their powerful ability to model data topology. We argue that the …

Fall happens when a person's movement coordination is disturbed, forcing them to rest on
the ground unintentionally causing serious health risks. The objective of this work is to …

We consider the problem of semi-supervised 3D action recognition which has been rarely
explored before. Its major challenge lies in how to effectively learn motion representations …

Compared to RGB video-based action recognition, skeleton-based action recognition
algorithm has attracted much more attention due to being more lightweight, better …

Human action recognition has been applied in many fields, such as video surveillance and
human computer interaction, where it helps to improve performance. Numerous reviews of …