Research
Simon-SR: Spatially Adaptive Modulation and Visual Prompt Adaptation for Text-Reinforced Super-Resolution
arXiv:2607.09351v2 Announce Type: replace Abstract: Severe downsampling makes single image super-resolution (SISR) an ill-posed problem, in which the language-guided multi-modal methods are especially
arXiv:2607.09351v2 Announce Type: replace Abstract: Severe downsampling makes single image super-resolution (SISR) an ill-posed problem, in which the language-guided multi-modal methods are especially vulnerable to erroneous priors and require costly textual annotations. To address these issues, we propose Simon-SR, a multi-modal SISR framework leveraging learnable prompts for efficient semantic mining and robust text-image fusion. Simon-SR treats textual semantics as learnable latent variables. Specifically, the Contrastive Prompt Learning (CPL) mines instance-level semantics from unannotated images with frozen CLIP encoders, and Prompt-Guided Spatially Adaptive Refinement (PSAR) injects them through attention-gated multi-modal fusion. On CUB and COCO2017 at imes4 and imes16, Simon-SR gains up to 0.50 dB PSNR and 0.0133 SSIM over current SOTA. The project is available at https://github.com/CHT05017/ICAIS26-SimonSR
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Source: arXiv cs.CV | 2026-10-01