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.

翻译：基于骨骼的零样本动作识别旨在通过学习已知骨骼动作的先验知识以及共享于已知与未知类别间的语义描述空间，实现对未知人体动作的识别。然而，现有方法侧重于在粗粒度层面建立已知骨骼表示空间与语义描述空间之间的桥梁以识别未知动作类别，忽略了两者间的细粒度对齐，导致在区分高相似度动作类别时性能欠佳。针对上述挑战，本文提出一种基于侧信息与双提示学习的细粒度骨骼零样本动作识别方法（STAR）。具体而言：1）基于骨骼拓扑结构将其分解为多个部分，并引入人体运动多部分描述的侧信息，在细粒度层面实现骨骼空间与语义空间的对齐；2）设计视觉属性提示与语义部位提示，分别提升骨骼空间内类内紧凑性和语义空间内类间可分性，以区分高相似度动作。大量实验表明，在NTU RGB+D、NTU RGB+D 120及PKU-MMD数据集上，本方法在零样本学习与广义零样本学习场景下均达到最优性能。