  The self-supervised pretraining paradigm has achieved great success in
skeleton-based action recognition. However, these methods treat the motion and
static parts equally, and lack an adaptive design for different parts, which
has a negative impact on the accuracy of action recognition. To realize the
adaptive action modeling of both parts, we propose an Actionlet-Dependent
Contrastive Learning method (ActCLR). The actionlet, defined as the
discriminative subset of the human skeleton, effectively decomposes motion
regions for better action modeling. In detail, by contrasting with the static
anchor without motion, we extract the motion region of the skeleton data, which
serves as the actionlet, in an unsupervised manner. Then, centering on
actionlet, a motion-adaptive data transformation method is built. Different
data transformations are applied to actionlet and non-actionlet regions to
introduce more diversity while maintaining their own characteristics.
Meanwhile, we propose a semantic-aware feature pooling method to build feature
representations among motion and static regions in a distinguished manner.
Extensive experiments on NTU RGB+D and PKUMMD show that the proposed method
achieves remarkable action recognition performance. More visualization and
quantitative experiments demonstrate the effectiveness of our method. Our
project website is available at https://langlandslin.github.io/projects/ActCLR/
