SC-Diff: Semantically Calibrated Diffusion for Visible-to-Infrared Image Translation
Visible-to-infrared image translation provides a practical way to expand infrared training data using abundant visible images. Diffusion models are promising for this task because of their strong generative performance. However, existing diffusion-based methods typically use semantic priors only as external conditions, without explicitly regulating token interactions within the denoising network. Consequently, they struggle to preserve object locations, shapes, and semantic layouts required for reliable annotation reuse. We propose SC-Diff, a semantically calibrated latent diffusion framework that uses semantic priors for both conditional guidance and internal self-attention calibration. ...
| Authors | Junyin Zhang, Siyu Huang, Jianxiong Ye et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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