  In the realm of skeleton-based action recognition, the traditional methods
which rely on coarse body keypoints fall short of capturing subtle human
actions. In this work, we propose Expressive Keypoints that incorporates hand
and foot details to form a fine-grained skeletal representation, improving the
discriminative ability for existing models in discerning intricate actions. To
efficiently model Expressive Keypoints, the Skeleton Transformation strategy is
presented to gradually downsample the keypoints and prioritize prominent joints
by allocating the importance weights. Additionally, a plug-and-play Instance
Pooling module is exploited to extend our approach to multi-person scenarios
without surging computation costs. Extensive experimental results over seven
datasets present the superiority of our method compared to the state-of-the-art
for skeleton-based human action recognition. Code is available at
https://github.com/YijieYang23/SkeleT-GCN.
