Flow Divergence Sampler refines flow matching by computing velocity field divergence to correct ambiguous intermediate states during inference, improving fidelity in text-to-image and inverse problem tasks.
Advances in neural information processing systems29(2016)
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MULTI uses two-stage textual inversion to disentangle camera lens, sensor, view, and domain factors for novel image generation, supporting dataset extension and ControlNet modifications on the new DF-RICO benchmark.
Geometry-preserving losses based on tangent-space distances improve blackbox GAN adaptation to shifted distributions compared with standard losses.
DepthPilot generates physically consistent and clinically interpretable colonoscopy videos by injecting depth priors into diffusion models through parameter-efficient fine-tuning and replacing linear denoising weights with adaptive splines.
EEG2Vision reconstructs images from EEG using diffusion models plus LLM-guided boosting, with reconstruction quality holding up reasonably as electrode count drops from 128 to 24 channels.
citing papers explorer
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Training-Free Refinement of Flow Matching with Divergence-based Sampling
Flow Divergence Sampler refines flow matching by computing velocity field divergence to correct ambiguous intermediate states during inference, improving fidelity in text-to-image and inverse problem tasks.
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MULTI: Disentangling Camera Lens, Sensor, View, and Domain for Novel Image Generation
MULTI uses two-stage textual inversion to disentangle camera lens, sensor, view, and domain factors for novel image generation, supporting dataset extension and ControlNet modifications on the new DF-RICO benchmark.
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Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Model
Geometry-preserving losses based on tangent-space distances improve blackbox GAN adaptation to shifted distributions compared with standard losses.
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DepthPilot: From Controllability to Interpretability in Colonoscopy Video Generation
DepthPilot generates physically consistent and clinically interpretable colonoscopy videos by injecting depth priors into diffusion models through parameter-efficient fine-tuning and replacing linear denoising weights with adaptive splines.
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EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience
EEG2Vision reconstructs images from EEG using diffusion models plus LLM-guided boosting, with reconstruction quality holding up reasonably as electrode count drops from 128 to 24 channels.