Part-Aware Unified Representation of Language and Skeleton for Zero-Shot Action Recognition

While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored. In this pa-per, we argue that relying solely on aligning label-level se-mantics and global skeleton features is insufficient to effectively t...

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Vydáno v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 18761 - 18770
Hlavní autoři: Zhu, Anqi, Ke, Qiuhong, Gong, Mingming, Bailey, James
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 16.06.2024
Témata:
action recognition
Computer vision
contrastive learning
large language model
Large language models
Natural languages
Pattern recognition
representation learning
Skeleton
skeleton action recognition
Source coding
Visualization
zero-shot learning
ISSN:1063-6919
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Shrnutí:While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored. In this pa-per, we argue that relying solely on aligning label-level se-mantics and global skeleton features is insufficient to effectively transfer locally consistent visual knowledge from seen to unseen classes. To address this limitation, we intro-duce 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 gen-erate aligned textual and visual representations across dif-ferent 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 skele-ton/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/azzhl/PURLS.
ISSN:1063-6919
DOI:10.1109/CVPR52733.2024.01775