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MLLMs still struggle with the spatiotemporal reasoning needed to understand surgical videos, even with chain-of-thought prompting.
MLLMs still struggle to integrate diverse data for clinical reasoning, as evidenced by their poor performance on a new ophthalmology benchmark spanning image quality assessment to diagnosis.
Gaze, often overlooked, reveals deepfake origins with surprising accuracy, enabling a new CLIP-based approach that significantly boosts deepfake attribution and detection.
DINOv3, a vision foundation model trained on general images, surprisingly excels at dental image analysis, especially for the notoriously difficult task of intraoral image understanding.
