-
[pdf]
[supp]
[bibtex]@InProceedings{Zhu_2024_CVPR, author = {Zhu, Anqi and Ke, Qiuhong and Gong, Mingming and Bailey, James}, title = {Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {18761-18770} }
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
Abstract
While remarkable progress has been made on supervised skeleton-based action recognition the challenge of zero-shot recognition remains relatively unexplored. In this paper we argue that relying solely on aligning label-level semantics and global skeleton features is insufficient to effectively transfer locally consistent visual knowledge from seen to unseen classes. To address this limitation we introduce Part-aware Unified Representation between Language and Skeleton (PURLS) to explore visual-semantic alignment at both local and global scales. PURLS introduces a new prompting module and a novel partitioning module to generate aligned textual and visual representations across different levels. The former leverages a pre-trained GPT-3 to infer refined descriptions of the global and local (body-part-based and temporal-interval-based) movements from the original action labels. The latter employs an adaptive sampling strategy to group visual features from all body joint movements that are semantically relevant to a given description. Our approach is evaluated on various skeleton/language backbones and three large-scale datasets i.e. NTU-RGB+D 60 NTU-RGB+D 120 and a newly curated dataset Kinetics-skeleton 200. The results showcase the universality and superior performance of PURLS surpassing prior skeleton-based solutions and standard baselines from other domains. The source codes can be accessed at https://github.com/azzh1/PURLS.
Related Material