  Dynamic skeletal data, represented as the 2D/3D coordinates of human joints,
has been widely studied for human action recognition due to its high-level
semantic information and environmental robustness. However, previous methods
heavily rely on designing hand-crafted traversal rules or graph topologies to
draw dependencies between the joints, which are limited in performance and
generalizability. In this work, we present a novel decoupled spatial-temporal
attention network(DSTA-Net) for skeleton-based action recognition. It involves
solely the attention blocks, allowing for modeling spatial-temporal
dependencies between joints without the requirement of knowing their positions
or mutual connections. Specifically, to meet the specific requirements of the
skeletal data, three techniques are proposed for building attention blocks,
namely, spatial-temporal attention decoupling, decoupled position encoding and
spatial global regularization. Besides, from the data aspect, we introduce a
skeletal data decoupling technique to emphasize the specific characteristics of
space/time and different motion scales, resulting in a more comprehensive
understanding of the human actions.To test the effectiveness of the proposed
method, extensive experiments are conducted on four challenging datasets for
skeleton-based gesture and action recognition, namely, SHREC, DHG, NTU-60 and
NTU-120, where DSTA-Net achieves state-of-the-art performance on all of them.
