Three-dimensional skeleton-based action recognition (3D SAR) has gained important
attention within the computer vision community, owing to the inherent advantages offered by …

Human skeleton-based action recognition offers a valuable means to understand the
intricacies of human behavior because it can handle the complex relationships between …

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

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

Human activity recognition (HAR) is an important research problem in computer vision. This
problem is widely applied to building applications in human–machine interactions …

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 …

Transformer models have shown great success handling long-range interactions, making
them a promising tool for modeling video. However, they lack inductive biases and scale …

Skeleton-based gesture recognition methods have achieved high success using Graph
Convolutional Network (GCN), which commonly uses an adjacency matrix to model the …

Many skeletal action recognition models use GCNs to represent the human body by 3D
body joints connected body parts. GCNs aggregate one-or few-hop graph neighbourhoods …

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