Few-Shot Concept Prompt Learning for Segmentation Foundation Models via Visual Grounding
Promptable segmentation foundation models (FMs) such as SAM3 and Medical SAM3 promise few-shot, interactively-specified segmentation for medical imaging through a natural language interface, yet their performance on clinical tasks falls well short of this promise. We posit that this shortfall is not an artefact of insufficient medical pretraining or imperfect prompt phrasing, but a structural limitation that will persist in any domain where paired image-text supervision is scarce, as it is across most clinical modalities. We further hypothesize that the limitation is specific to natural language as a control signal: a visually grounded prompt, learned directly from the target distribution...
| Authors | Rahul Venkataramani, Rachana Sathish |
|---|---|
| Primary category | cs.CV |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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