Prompt-Anchored Residual Adaptation for Biomedical Vision-Language Models
Pretrained biomedical vision-language models achieve strong zero-shot performance in biomedical image classification. However, downstream biomedical classification often depends on subtle visual differences between classes that may not be fully captured by pretrained representations. Few-shot adaptation addresses this mismatch by optimizing a task-specific predictor on a small labeled support set. Because the selected examples capture only part of the visual variation within the target classes, the adapted predictions can depend strongly on their composition. We propose Prompt-Anchored Residual Adaptation (PARA), which retains the frozen prompt prediction as a support-invariant semantic a...
| Authors | Jingxuan Kang, Qianying Yue, Che Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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