Knowledge-guided Disentanglement with Atomic Actions for Action Recognition
Action recognition in complex scenes often involves multiple concurrent fine-grained actions, making it challenging to model internal action structures. Most existing methods rely on holistic representations, which are insufficient for capturing subtle interactions and fine-grained semantics. While recent prompt-based approaches introduce disentanglement, they lack explicit semantic guidance, and methods based solely on visual or structured cues remain coarse-grained. In this paper, we propose Knowledge-guided Disentanglement with Atomic Actions (KDA), which leverages fine-grained semantic knowledge to enhance action representations and enable more precise disentanglement. Specifically, w...
| Authors | Tianci Wu, Siqi Cao, Guangming Zhu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 July 2026 |
| Last updated at source | 4 August 2026 |
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