  Skeleton-based zero-shot action recognition aims to recognize unknown human
actions based on the learned priors of the known skeleton-based actions and a
semantic descriptor space shared by both known and unknown categories. However,
previous works focus on establishing the bridges between the known skeleton
representation space and semantic descriptions space at the coarse-grained
level for recognizing unknown action categories, ignoring the fine-grained
alignment of these two spaces, resulting in suboptimal performance in
distinguishing high-similarity action categories. To address these challenges,
we propose a novel method via Side information and dual-prompts learning for
skeleton-based zero-shot action recognition (STAR) at the fine-grained level.
Specifically, 1) we decompose the skeleton into several parts based on its
topology structure and introduce the side information concerning multi-part
descriptions of human body movements for alignment between the skeleton and the
semantic space at the fine-grained level; 2) we design the visual-attribute and
semantic-part prompts to improve the intra-class compactness within the
skeleton space and inter-class separability within the semantic space,
respectively, to distinguish the high-similarity actions. Extensive experiments
show that our method achieves state-of-the-art performance in ZSL and GZSL
settings on NTU RGB+D, NTU RGB+D 120, and PKU-MMD datasets.
