  Skeleton-based action recognition is vital for comprehending human-centric
videos and has applications in diverse domains. One of the challenges of
skeleton-based action recognition is dealing with low-quality data, such as
skeletons that have missing or inaccurate joints. This paper addresses the
issue of enhancing action recognition using low-quality skeletons through a
general knowledge distillation framework. The proposed framework employs a
teacher-student model setup, where a teacher model trained on high-quality
skeletons guides the learning of a student model that handles low-quality
skeletons. To bridge the gap between heterogeneous high-quality and lowquality
skeletons, we present a novel part-based skeleton matching strategy, which
exploits shared body parts to facilitate local action pattern learning. An
action-specific part matrix is developed to emphasize critical parts for
different actions, enabling the student model to distill discriminative
part-level knowledge. A novel part-level multi-sample contrastive loss achieves
knowledge transfer from multiple high-quality skeletons to low-quality ones,
which enables the proposed knowledge distillation framework to include training
low-quality skeletons that lack corresponding high-quality matches.
Comprehensive experiments conducted on the NTU-RGB+D, Penn Action, and SYSU 3D
HOI datasets demonstrate the effectiveness of the proposed knowledge
distillation framework.
