Rectified flow learns straight-path neural ODEs for distribution transport, yielding efficient generative models and domain transfers that work well even with a single simulation step.
Dual diffusion implicit bridges for image-to-image translation
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OT-Bridge Editor uses geometrically constrained entropic optimal transport to synthesize CAG images with precise stenosis, improving downstream detection by 27.8% on ARCADE and 23.0% on a multi-center dataset.
Doloris introduces dual conditional diffusion implicit bridges plus a sparsity masking strategy to model unpaired single-cell perturbation responses and reports state-of-the-art results on public datasets.
T-CLIP introduces a physics-aware thermal captioning dataset (IR-Cap) and a decoupled dual-LoRA adaptation of CLIP that improves cross-modal retrieval on thermal benchmarks by separating scene-level and object-level thermal understanding.
DMSM proposes a self-supervised dual-domain multi-path diffusion framework for accelerated MRI reconstruction that removes the need for fully sampled training data while providing uncertainty maps.
Stochastic image enhancement methods are shown to be variants of a shared SDE differing in drift, diffusion, terminal distributions and boundary conditions, with controlled experiments revealing no single dominant family and a new modular library released.
MedShift applies flow matching and Schrödinger bridges for class-conditional unpaired translation between synthetic and real skull X-rays, benchmarked on the new X-DigiSkull dataset.
citing papers explorer
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Geometrically Constrained Stenosis Editing in Coronary Angiography via Entropic Optimal Transport
OT-Bridge Editor uses geometrically constrained entropic optimal transport to synthesize CAG images with precise stenosis, improving downstream detection by 27.8% on ARCADE and 23.0% on a multi-center dataset.
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T-CLIP: Enabling Thermal Perception for Contrastive Language-Image Pretraining
T-CLIP introduces a physics-aware thermal captioning dataset (IR-Cap) and a decoupled dual-LoRA adaptation of CLIP that improves cross-modal retrieval on thermal benchmarks by separating scene-level and object-level thermal understanding.
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Unifying Deep Stochastic Processes for Image Enhancement
Stochastic image enhancement methods are shown to be variants of a shared SDE differing in drift, diffusion, terminal distributions and boundary conditions, with controlled experiments revealing no single dominant family and a new modular library released.
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MedShift: Implicit Conditional Transport for X-Ray Domain Adaptation
MedShift applies flow matching and Schrödinger bridges for class-conditional unpaired translation between synthetic and real skull X-rays, benchmarked on the new X-DigiSkull dataset.