Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

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 …

Human action recognition aims at classifying the category of human action from a segment
of a video. Recently, people have dived into designing GCN-based models to extract …

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

Graph convolutional networks (GCNs) are the most commonly used methods for skeleton-
based action recognition and have achieved remarkable performance. Generating …

Graph convolutional networks (GCNs) have been widely used and achieved remarkable
results in skeleton-based action recognition. In GCNs, graph topology dominates feature …

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 …

Abstract Graph Convolutional Networks (GCNs) have long set the state-of-the-art in skeleton-
based action recognition leveraging their ability to unravel the complex dynamics of human …

In the semi-supervised skeleton-based action recognition task, obtaining more
discriminative information from both labeled and unlabeled data is a challenging problem …

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 …