CLIP-Guided Unsupervised Semantic-Aware Exposure Correction

• URL: http://arxiv.org/abs/2601.19129v2
• Date: Wed, 28 Jan 2026 11:08:04 GMT
• Title: CLIP-Guided Unsupervised Semantic-Aware Exposure Correction
• Authors: Puzhen Wu, Han Weng, Quan Zheng, Yi Zhan, Hewei Wang, Yiming Li, Jiahui Han, Rui Xu,
• Abstract summary: A new unsupervised semantic-aware exposure correction network is proposed.<n>It fuses semantic information extracted from a pre-trained Fast Segment Anything Model into a shared image feature space.<n>A pseudo-ground truth generator guided by CLIP is fine-tuned to automatically identify exposure situations and instruct the tailored corrections.
• Score: 13.05173129182012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improper exposure often leads to severe loss of details, color distortion, and reduced contrast. Exposure correction still faces two critical challenges: (1) the ignorance of object-wise regional semantic information causes the color shift artifacts; (2) real-world exposure images generally have no ground-truth labels, and its labeling entails massive manual editing. To tackle the challenges, we propose a new unsupervised semantic-aware exposure correction network. It contains an adaptive semantic-aware fusion module, which effectively fuses the semantic information extracted from a pre-trained Fast Segment Anything Model into a shared image feature space. Then the fused features are used by our multi-scale residual spatial mamba group to restore the details and adjust the exposure. To avoid manual editing, we propose a pseudo-ground truth generator guided by CLIP, which is fine-tuned to automatically identify exposure situations and instruct the tailored corrections. Also, we leverage the rich priors from the FastSAM and CLIP to develop a semantic-prompt consistency loss to enforce semantic consistency and image-prompt alignment for unsupervised training. Comprehensive experimental results illustrate the effectiveness of our method in correcting real-world exposure images and outperforms state-of-the-art unsupervised methods both numerically and visually.

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