FlowDIS uses flow matching to transport image distributions to mask distributions, optionally conditioned on text, and outperforms prior DIS methods by 5.5% on F_beta^omega and 43% on MAE.
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DeCo decouples high- and low-frequency generation in pixel diffusion via a DiT plus lightweight decoder and a frequency-aware flow-matching loss, reaching FID 1.62 at 256x256 and 2.22 at 512x512 on ImageNet while closing the gap to latent diffusion methods.
IR-Flow uses rectified flow to build linear transport between degraded and clean images via multilevel flows and cumulative velocity fields, enabling competitive restoration quality with only a few sampling steps.
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FlowDIS: Language-Guided Dichotomous Image Segmentation with Flow Matching
FlowDIS uses flow matching to transport image distributions to mask distributions, optionally conditioned on text, and outperforms prior DIS methods by 5.5% on F_beta^omega and 43% on MAE.
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DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation
DeCo decouples high- and low-frequency generation in pixel diffusion via a DiT plus lightweight decoder and a frequency-aware flow-matching loss, reaching FID 1.62 at 256x256 and 2.22 at 512x512 on ImageNet while closing the gap to latent diffusion methods.
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IR-Flow: Bridging Discriminative and Generative Image Restoration via Rectified Flow
IR-Flow uses rectified flow to build linear transport between degraded and clean images via multilevel flows and cumulative velocity fields, enabling competitive restoration quality with only a few sampling steps.