  Recently, Transformer-based networks have shown great promise on
skeleton-based action recognition tasks. The ability to capture global and
local dependencies is the key to success while it also brings quadratic
computation and memory cost. Another problem is that previous studies mainly
focus on the relationships among individual joints, which often suffers from
the noisy skeleton joints introduced by the noisy inputs of sensors or
inaccurate estimations. To address the above issues, we propose a novel
Transformer-based network (IIP-Transformer). Instead of exploiting interactions
among individual joints, our IIP-Transformer incorporates body joints and parts
interactions simultaneously and thus can capture both joint-level (intra-part)
and part-level (inter-part) dependencies efficiently and effectively. From the
data aspect, we introduce a part-level skeleton data encoding that
significantly reduces the computational complexity and is more robust to
joint-level skeleton noise. Besides, a new part-level data augmentation is
proposed 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 achieves
the-state-of-art performance with more than 8x less computational complexity
than DSTA-Net, which is the SOTA Transformer-based method.
