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CRISP significantly boosts retrieval accuracy for clinical decision-making by effectively filtering out uninformative flowchart regions.
Visual in-context learning models struggle with adaptation, revealing critical limitations across 106 dataset-task combinations.
Correcting errors in long-video understanding doesn't have to be a nightmare: IMPACT-CYCLE slashes human arbitration costs by 4.8x while boosting VQA accuracy by intelligently decomposing the task and focusing human effort where it matters most.
Medical imaging tasks like segmentation and denoising aren't as different as you think: a contrastive embedding reveals surprising relationships across modalities.
