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.
Gmflow: Learning optical flow via global matching
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Swift Sampling is a training-free frame selection method that uses Taylor expansions on video latent trajectories to pick temporally surprising frames, outperforming uniform sampling on long-video QA tasks.
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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Swift Sampling: Selecting Temporal Surprises via Taylor Series
Swift Sampling is a training-free frame selection method that uses Taylor expansions on video latent trajectories to pick temporally surprising frames, outperforming uniform sampling on long-video QA tasks.
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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.