DoGCLR: Dominance-Game Contrastive Learning Network for Skeleton-Based Action Recognition
Existing self-supervised contrastive learning methods for skeleton-based action recognition typically treat skeletal regions uniformly and rely on FIFO queues for negative sample storage, leading to loss of motion details and suboptimal negative sample selection. To address these issues, this paper proposes a dominance-gaming–based self-supervised contrastive learning framework. First, it models dynamic dominance relationships between positive and negative samples to enhance representation discriminability and semantic consistency. Second, it introduces spatiotemporal dual-dimensional weighted region localization and region-level data augmentation to preserve critical motion structures. Third, it incorporates an entropy-driven hard-negative memory bank with dynamic updating to improve negative sample quality. Extensive experiments demonstrate state-of-the-art performance: on NTU RGB+D, improvements of 1.1% and 2.3% are achieved on NTU120 X-Sub and X-Set benchmarks, respectively; on PKU-MMD Part II, the method achieves a 1.9% gain over prior art.