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Recently, Transformer-based networks have shown great promise onskeleton-based action recognition tasks. The ability to capture global andlocal dependencies is the key to success while it also brings quadraticcomputation and memory cost. Another problem is that previous studies mainlyfocus on the relationships among individual joints, which often suffers fromthe noisy skeleton joints introduced by the noisy inputs of sensors orinaccurate estimations. To address the above issues, we propose a novelTransformer-based network (IIP-Transformer). Instead of exploiting interactionsamong individual joints, our IIP-Transformer incorporates body joints and partsinteractions simultaneously and thus can capture both joint-level (intra-part)and part-level (inter-part) dependencies efficiently and effectively. From thedata aspect, we introduce a part-level skeleton data encoding thatsignificantly reduces the computational complexity and is more robust tojoint-level skeleton noise. Besides, a new part-level data augmentation isproposed to improve the performance of the model. On two large-scale datasets,NTU-RGB+D 60 and NTU RGB+D 120, the proposed IIP-Transformer achievesthe-state-of-art performance with more than 8x less computational complexitythan DSTA-Net, which is the SOTA Transformer-based method.

IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition