Supervised human action recognition methods based on skeleton data have achieved
impressive performance recently. However, many current works emphasize the design of …

This paper introduces a novel concept for Home-based Monitoring (HM) that enables robust
analysis and understanding of activities towards improved caring and safety. Spatio …

Contrastive learning has achieved great success in skeleton-based action recognition.
However, most existing approaches encode the skeleton sequences as entangled …

Skeleton-based action recognition is a crucial but challenging task in the application of
engineering algorithms. However, due to the inaccurate estimation quality, certain joints that …

This paper proposes a novel approach for few-shot skeleton action recognition that
comprises of two stages: cross-modal pre-training of a skeleton encoder, followed by fine …

Skeleton-based action recognition is crucial for natural human-computer interaction,
dynamic behavior analysis, and behavior surveillance. The key challenge is to effectively …

The existing approaches for skeleton-based action recognition based on graph
convolutional networks (GCNs) primarily emphasize the construction of human skeletal …

Deep neural networks for skeleton-based human action recognition (HAR) often utilize
traditional averaging or maximum temporal pooling to aggregate features by treating all …

Human 3D skeleton-based action recognition has received increasing interest in recent
years. Inspired by the excellent ability of the multi-modal model, some pioneer attempts to …

Skeleton representation has garnered considerable attention due to its robust and compact
depiction of human actions. Recently, Graph Convolutional Networks (GCNs) have become …