Image Fusion via Vision-Language Model

Zixiang Zhao, Lilun Deng, Haowen Bai, Yukun Cui, Zhipeng Zhang, Yulun Zhang, Haotong Qin, Dongdong Chen, Jiangshe Zhang, Peng Wang, Luc Van Gool

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9 引用 (Scopus)

摘要

Image fusion integrates essential information from multiple images into a single composite, enhancing structures, textures, and refining imperfections.Existing methods predominantly focus on pixel-level and semantic visual features for recognition, but often overlook the deeper text-level semantic information beyond vision.Therefore, we introduce a novel fusion paradigm named image Fusion via vIsion-Language Model (FILM), for the first time, utilizing explicit textual information from source images to guide the fusion process.Specifically, FILM generates semantic prompts from images and inputs them into ChatGPT for comprehensive textual descriptions.These descriptions are fused within the textual domain and guide the visual information fusion, enhancing feature extraction and contextual understanding, directed by textual semantic information via cross-attention.FILM has shown promising results in four image fusion tasks: infrared-visible, medical, multi-exposure, and multi-focus image fusion.We also propose a vision-language dataset containing ChatGPT-generated paragraph descriptions for the eight image fusion datasets across four fusion tasks, facilitating future research in vision-language model-based image fusion.Code and dataset are available at https://github.com/Zhaozixiang1228/IF-FILM.

源语言英语
页(从-至)60749-60765
页数17
期刊Proceedings of Machine Learning Research
235
出版状态已出版 - 2024
活动41st International Conference on Machine Learning, ICML 2024 - Vienna, 奥地利
期限: 21 7月 202427 7月 2024

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