EAPFusion uses self-evolving intrinsic priors to produce dynamic, scene-adaptive convolution kernels and channel-mixing fusion for infrared-visible images, reporting state-of-the-art results and downstream gains.
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New knot classification benchmark and topology-aware supervision methods yield small specificity gains but confirm that appearance bias remains the dominant failure mode.
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.
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EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion
EAPFusion uses self-evolving intrinsic priors to produce dynamic, scene-adaptive convolution kernels and channel-mixing fusion for infrared-visible images, reporting state-of-the-art results and downstream gains.
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Physical Knot Classification Beyond Accuracy: A Benchmark and Diagnostic Study
New knot classification benchmark and topology-aware supervision methods yield small specificity gains but confirm that appearance bias remains the dominant failure mode.
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Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.