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Sparsifying attention maps based on channel-wise correlations unlocks state-of-the-art performance in hyperspectral image super-resolution while maintaining competitive efficiency.
Achieve state-of-the-art SFUDA in medical image segmentation by distilling knowledge from vision foundation models with context-aware prompts and visual plausibility refinement, outperforming existing methods across 10 domain adaptation directions and 22 anatomical targets.
