Vision Harnessing Agent for Open Ad-hoc Segmentation
Segmentation has become easy when the concept is known, requiring retrieval of a learned visual grounding from text. It remains hard for open ad-hoc concepts, where the grounding may not exist as one learned mask and must often be constructed from image evidence through parts, relations, exclusions, and collections. We propose a Vision-guided Ad-hoc Segmentation Agent (VASA), the first vision harnessing agent for open ad-hoc segmentation. VASA is training-free and couples a VLM agent, a segmentation foundation model, and a visual harness that maintains a working mask to make visual progress persistent, inspectable, and editable. Rather than revising text prompts alone, it plans visual ope...
| Authors | Zilin Wang, Stella X. Yu |
|---|---|
| Primary category | cs.CV |
| Published | 19 May 2026 |
| Last updated at source | 29 September 2026 |
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