Dynamic Graph Attention Network for Skeleton-Based Action Recognition

Abstract

Skeleton-based human action recognition has garnered significant attention for its robustness to background noise and illumination variations. However, existing methods relying on Graph Convolutional Networks (GCNs) and Transformers exhibit inherent limitations: GCNs struggle to model interactions between non-adjacent joints due to predefined skeletal topology, while Transformers accumulate noise through unrestricted global dependency modeling. To address these challenges, we propose a Dynamic Graph Attention Network (DGAN) that dynamically integrates local structural features and global spatiotemporal dependencies. DGAN employs a masked attention mechanism to adaptively adjust node connectivity, forming a dynamic adjacency matrix that extends beyond physical skeletal constraints by selectively incorporating highly correlated joints. Additionally, a node-partition bias strategy is introduced to prioritize attention on collaboratively moving body parts, thereby enhancing discriminative feature extraction. Extensive experiments on the NTU RGB+D 60 and NTU RGB+D 120 datasets validate the effectiveness of DGAN, which outperforms state-of-the-art methods by achieving a balance between local topology preservation and global interaction modeling. Our approach provides a robust framework for skeleton-driven action recognition, demonstrating superior generalization across diverse scenarios.