Global and Local Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition

Contrastive learning for self-supervised skeleton-based action recognition has recently received attention. It has been observed that local crops, containing partial action sequences, can predict action patterns, which is advantageous for skeleton-based action recognition. This paper proposes a Global and Local Contrastive Learning framework (skeleton-logoCLR) with two contrastive learning routes, Global-to-Global and Global-to-Local, which utilize the similarity between global and local crops of the same skeleton sequence. Specifically, in the Global-to-Global route, we design Temporal Attention Crop-Resize (TACR) to learn global semantic information by maximizing the retention of action region in the temporal dimension. In the Global-to-Local route, the proposed Skeleton-logo Augmentation is deviced to concatenate two local crops from different sequences for local semantic learning. Moreover, instead of fusing directly, the losses of two routes are combined in a cascaded manner through the Self-Adaptive Training Strategy (SATS) to achieve stronger generalization performance. Extensive experiments are conducted on the NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets. The results demonstrate that the proposed method achieves remarkable performance.