AI RESEARCH
Adapting Large VLMs with Iterative and Manual Instructions for Generative Low-light Enhancement
arXiv CS.CV
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ArXi:2507.18064v2 Announce Type: replace Most existing low-light image enhancement (LLIE) methods rely on pre-trained model priors, low-light inputs, or both, while neglecting the semantic guidance available from normal-light images. This limitation hinders their effectiveness in complex lighting conditions. In this paper, we propose VLM-IMI, a framework that adapts large vision-language models with iterative and manual instructions for generative