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 this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D
skeleton-based action representation (CrosSCLR), by leveraging multi-view complementary …

Abstract Human Action Recognition (HAR) is a popular area of research in computer vision
due to its wide range of applications such as surveillance, health care, and gaming, etc …

Abstract Human Action Recognition (HAR) has achieved a remarkable milestone in the field
of computer vision. Apart from its varied applications in human–computer interactions …

In 3D action recognition, there exists rich complementary information between skeleton
modalities. Nevertheless, how to model and utilize this information remains a challenging …

This paper targets unsupervised skeleton-based action representation learning and
proposes a new Hierarchical Contrast (HiCo) framework. Different from the existing …

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 …

Deep learning models have been widely used for anomaly detection in surveillance videos.
Typical models are equipped with the capability to reconstruct normal videos and evaluate …

Skeleton representation has attracted a great deal of attention recently as an extremely
robust feature for human action recognition. However, its non-Euclidean structural …

In the task of human motion recognition, the overall action span is changeable, and there
may be an inclusion relationship between action semantics. This paper proposes a novel …