ADM uses interdependent score-based diffusion models and iterative Langevin sampling to achieve state-of-the-art alignment of SFI-UWFI retinal image pairs, with reported mAUC gains of 5.2 and 0.4 points over prior methods.
Separable flow: Learning motion cost volumes for optical flow esti- mation
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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UNVERDICTED 2representative citing papers
Particle Diffusion Matching uses diffusion-guided random walk searches to align challenging standard and ultra-widefield retinal images, claiming state-of-the-art benchmark performance.
citing papers explorer
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Active Diffusion Matching: Score-based Iterative Alignment of Cross-Modal Retinal Images
ADM uses interdependent score-based diffusion models and iterative Langevin sampling to achieve state-of-the-art alignment of SFI-UWFI retinal image pairs, with reported mAUC gains of 5.2 and 0.4 points over prior methods.
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Particle Diffusion Matching: Random Walk Correspondence Search for the Alignment of Standard and Ultra-Widefield Fundus Images
Particle Diffusion Matching uses diffusion-guided random walk searches to align challenging standard and ultra-widefield retinal images, claiming state-of-the-art benchmark performance.