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 …

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

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 …

Skeleton-based action recognition has attracted considerable attention since the skeleton
data is more robust to the dynamic circumstances and complicated backgrounds than other …

Abstract Graph Convolutional Networks (GCNs) have been widely used to model the high-
order dynamic dependencies for skeleton-based action recognition. Most existing …

We propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint
Temporal and Camera Viewpoint Alignment. To factor out misalignment between query and …

With the aid of graph convolution neural network and transformer model, human action
recognition has achieved significant performance based on skeleton data. However, the …

3D human action recognition has received increasing attention due to its potential
application in video surveillance equipment. To guarantee satisfactory performance …

Video sequences exhibit significant nuisance variations (undesired effects) of speed of
actions, temporal locations, and subjects' poses, leading to temporal-viewpoint …

Skeleton-based action recognition methods are limited by the semantic extraction of spatio-
temporal skeletal maps. However, current methods have difficulty in effectively combining …