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A novel framework, Distilling Image Prototype for Guided Test-Time Adaptation (DIPTTA), a compact set of synthetic images that serves as a dynamic and regenerative anchor of source knowledge that anchors a source-calibrated uncertainty estimation method.
DARD first extracts image-specific physical priors from the degraded input through a test-time degradation-aware Retinex decomposition, thereby providing reliable structural guidance for zero-shot restoration and injects these priors into reverse diffusion through a timestep-adaptive frequency fusion strategy to balanc...
This work proposes an uncertainty-aware efficient segmentation framework synergizing Mamba state-space models with evidential deep learning, employing a 2D-adapted selective state-space mechanism to capture long-range dependencies with linear complexity O(L), overcoming transformers' quadratic scaling.
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