GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition

arXiv:2403.15212v1 Announce Type: new
Abstract: Skeleton-based action recognition (SAR) in videos is an important but challenging task in computer vision. The recent state-of-the-art models for SAR are primarily based on graph convolutional neural networks (GCNs), which are powerful in extracting the spatial information of skeleton data. However, it is yet clear that such GCN-based models can effectively capture the temporal dynamics of human action sequences. To this end, we propose the DevLSTM module, which exploits the path development -- a …

action recognition arxiv cs.cv development path recognition type