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MedMLIP is introduced, a framework that pretrains the visual encoder through report generation with a frozen LLM, while employing Local Relational Distillation (LRD) to preserve relationships among visual patches to avoid visual collapse.
TCLA corrects inference logits based on a small set of support samples, boosting pretrained VLMs performance by improving inter-class deconfusion and reducing domain shift, and consistently improves OOD performance of Medical VLMs and outperforms existing training-based adaptation methods.
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