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CRISP significantly boosts retrieval accuracy for clinical decision-making by effectively filtering out uninformative flowchart regions.
A tiny VICL model challenges the assumption that bigger is always better, revealing critical gaps in how we evaluate adaptive capabilities in vision tasks.
Visual in-context learning models struggle with adaptation, revealing critical limitations across 106 dataset-task combinations.
User-provided cues like scribbles are often ignored by state-of-the-art visual in-context learners, but this work shows how to make them listen.
Medical imaging tasks like segmentation and denoising aren't as different as you think: a contrastive embedding reveals surprising relationships across modalities.
