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University of New South Wales, Mohamed bin Zayed University of Artificial Intelligence
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NLICV not only speeds up LLM personalization evaluation by up to 2100 times but also offers a clearer understanding of model behaviors beyond simple binary scoring.
Tailoring medical image segmentation models to dataset-specific requirements can boost performance by over 10% compared to traditional architecture-first approaches.
Forget choosing just one vision encoder – fusing CLIP and DINO representations unlocks a significant performance boost in vision-language tasks.
Achieve state-of-the-art video polyp segmentation by adaptively selecting informative reference frames and aggregating multi-scale historical features with causal attention.
Medical VLMs get a calibration boost without training or labels: LATA sharpens predictions by smoothing over a k-NN graph, shrinking prediction sets and balancing class coverage.
