Papers › Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
Anqi Zhu, Qiuhong Ke, Mingming Gong, James Bailey
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero Shot Skeletal Action Recognition | NTU RGB+D | PURLS | Accuracy (12 unseen classes) | 40.99 | #5 of 9 | Archive leaderboard | report |
| Zero Shot Skeletal Action Recognition | NTU RGB+D | PURLS | Accuracy (5 unseen classes) | 79.23 | #5 of 9 | Archive leaderboard | report |
| Zero Shot Skeletal Action Recognition | NTU RGB+D 120 | PURLS | Accuracy (10 unseen classes) | 71.95 | #3 of 9 | Archive leaderboard | report |
| Zero Shot Skeletal Action Recognition | NTU RGB+D 120 | PURLS | Accuracy (24 unseen classes) | 52.01 | #3 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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