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Distilling Textual Priors from LLM to Efficient Image Fusion by Zhang, Ran; He, Xuanhua; Cao, Ke; Liu, Liu; Zhang, Li; Zhou, Man; Zhang, Jie is a scholarly article available to read on EtoBox.

What is Distilling Textual Priors from LLM to Efficient Image Fusion about?

Multi-modality image fusion aims to synthesize a single, comprehensive image from multiple source inputs. Traditional approaches, such as CNNs and GANs, offer efficiency but struggle to handle low-quality or complex inputs. Recent advances in text-guided methods leverage large model priors to overcome these limitations, but at the cost of significant computational overhead, both in memory and inference time. To address this challenge, we propose a novel framework for distilling large model priors, eliminating the need for text guidance during inference while dramatically reducing model size. Our framework utilizes a teacher-student architecture, where the teacher network incorporates large model priors and transfers this knowledge to a smaller student network via a tailored distillation process. Additionally, we introduce spatial-channel cross-fusion module to enhance the model's ability to leverage textual priors across both spatial and channel dimensions. Our method achieves a favorable trade-off between computational efficiency and fusion quality. The distilled network, requiring only 10\% of the parameters and inference time of the teacher network, retains 90\% of its performan

Author

Zhang, Ran; He, Xuanhua; Cao, Ke; Liu, Liu; Zhang, Li; Zhou, Man; Zhang, Jie

Published

2025

Language

EN