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 …

Understanding actions in videos remains a significant challenge in computer vision, which
has been the subject of several pieces of research in the last decades. Convolutional neural …

We present a unified perspective on tackling various human-centric video tasks by learning
human motion representations from large-scale and heterogeneous data resources …

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 …

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

Human skeleton, as a compact representation of human action, has received increasing
attention in recent years. Many skeleton-based action recognition methods adopt GCNs to …

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 …