Compositional Context Fine-Tuning Vision-Language Model for Complex Assembly Action Understanding from Videos
Assembly action understanding is a key enabler for effective human-robot collaborative assembly, yet it remains challenging due to subtle motions and fine-grained hand-object interactions. We adapt vision-language models (VLMs) to this challenging domain with Compositional Context Fine-Tuning (CCFT), a method that decomposes assembly actions into semantic elements (Verb, Object, Tool) and fine-tunes VLMs to recognize each action element using templated question-answering pairs. This approach ensures near-deterministic outputs. To enable efficient and effective multi-task learning under limited data, a Layer-Partitioned Alternating Training (LP-AT) method is presented, which assigns distin...
| Authors | Hao Zheng, Jinyi Huang, Tiantian Zheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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