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Advances in neural information processing systems34, 8780–8794 (2021)

4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

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citation-polarity summary

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cs.CV 4

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2026 4

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UNVERDICTED 4

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representative citing papers

DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models

cs.CV · 2026-04-07 · unverdicted · novelty 6.0

DiffHDR converts LDR videos to HDR by formulating the task as generative radiance inpainting in a video diffusion model's latent space, using Log-Gamma encoding and synthesized training data to achieve better fidelity and stability than prior methods.

Dual-End Consistency Model

cs.CV · 2026-02-11 · unverdicted · novelty 6.0

DE-CM reaches state-of-the-art one-step FID of 1.70 on ImageNet 256x256 by decomposing PF-ODE trajectories into three critical sub-trajectories and using flow matching plus N2N mapping for stability.

SHIFT: Steering Hidden Intermediates in Flow Transformers

cs.CV · 2026-04-10 · unverdicted · novelty 5.0

SHIFT learns and applies steering vectors to selected layers and timesteps in DiT models to suppress concepts, shift styles, or bias objects while keeping image quality and prompt adherence intact.

citing papers explorer

Showing 4 of 4 citing papers.

  • VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation cs.CV · 2026-04-24 · unverdicted · none · ref 5

    VS-DDPM accelerates 3D diffusion models for medical modality translation, reaching SOTA Dice scores of 0.80-0.88 and SSIM 0.95 on missing MRI synthesis in BraTS2025 while remaining competitive on tumor removal and sCT tasks.

  • DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models cs.CV · 2026-04-07 · unverdicted · none · ref 16

    DiffHDR converts LDR videos to HDR by formulating the task as generative radiance inpainting in a video diffusion model's latent space, using Log-Gamma encoding and synthesized training data to achieve better fidelity and stability than prior methods.

  • Dual-End Consistency Model cs.CV · 2026-02-11 · unverdicted · none · ref 5

    DE-CM reaches state-of-the-art one-step FID of 1.70 on ImageNet 256x256 by decomposing PF-ODE trajectories into three critical sub-trajectories and using flow matching plus N2N mapping for stability.

  • SHIFT: Steering Hidden Intermediates in Flow Transformers cs.CV · 2026-04-10 · unverdicted · none · ref 4

    SHIFT learns and applies steering vectors to selected layers and timesteps in DiT models to suppress concepts, shift styles, or bias objects while keeping image quality and prompt adherence intact.