Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909957774901248 |
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| author | Zhu, Jingmin Zhu, Anqi Rahmani, Hossein Liu, Jun Bennamoun, Mohammed Ke, Qiuhong |
| author_facet | Zhu, Jingmin Zhu, Anqi Rahmani, Hossein Liu, Jun Bennamoun, Mohammed Ke, Qiuhong |
| contents | We introduce Skeleton-Cache, the first training-free test-time adaptation framework for skeleton-based zero-shot action recognition (SZAR), aimed at improving model generalization to unseen actions during inference. Skeleton-Cache reformulates inference as a lightweight retrieval process over a non-parametric cache that stores structured skeleton representations, combining both global and fine-grained local descriptors. To guide the fusion of descriptor-wise predictions, we leverage the semantic reasoning capabilities of large language models (LLMs) to assign class-specific importance weights. By integrating these structured descriptors with LLM-guided semantic priors, Skeleton-Cache dynamically adapts to unseen actions without any additional training or access to training data. Extensive experiments on NTU RGB+D 60/120 and PKU-MMD II demonstrate that Skeleton-Cache consistently boosts the performance of various SZAR backbones under both zero-shot and generalized zero-shot settings. The code is publicly available at https://github.com/Alchemist0754/Skeleton-Cache. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation Zhu, Jingmin Zhu, Anqi Rahmani, Hossein Liu, Jun Bennamoun, Mohammed Ke, Qiuhong Computer Vision and Pattern Recognition Artificial Intelligence We introduce Skeleton-Cache, the first training-free test-time adaptation framework for skeleton-based zero-shot action recognition (SZAR), aimed at improving model generalization to unseen actions during inference. Skeleton-Cache reformulates inference as a lightweight retrieval process over a non-parametric cache that stores structured skeleton representations, combining both global and fine-grained local descriptors. To guide the fusion of descriptor-wise predictions, we leverage the semantic reasoning capabilities of large language models (LLMs) to assign class-specific importance weights. By integrating these structured descriptors with LLM-guided semantic priors, Skeleton-Cache dynamically adapts to unseen actions without any additional training or access to training data. Extensive experiments on NTU RGB+D 60/120 and PKU-MMD II demonstrate that Skeleton-Cache consistently boosts the performance of various SZAR backbones under both zero-shot and generalized zero-shot settings. The code is publicly available at https://github.com/Alchemist0754/Skeleton-Cache. |
| title | Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2512.11458 |