@inproceedings{
chen2024finegrained,
title={Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition},
author={Yang Chen and Jingcai Guo and Tian He and Xiaocheng Lu and Ling Wang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=xcegM22L6D}
}
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 representations 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. The code will be available in the future.
Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition
Yang Chen Jingcai Guo Tian He Xiaocheng Lu Ling Wang