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Robust Fusion Controller: Degradation-aware Image Fusion with Fine-grained Language Instructions by Zhang, Hao; Zha, Yanping; Zhuang, Qingwei; Shao, Zhenfeng; Ma, Jiayi is a scholarly article available to read on EtoBox.

What is Robust Fusion Controller: Degradation-aware Image Fusion with Fine-grained Language Instructions about?

Current image fusion methods struggle to adapt to real-world environments encompassing diverse degradations with spatially varying characteristics. To address this challenge, we propose a robust fusion controller (RFC) capable of achieving degradation-aware image fusion through fine-grained language instructions, ensuring its reliable application in adverse environments. Specifically, RFC first parses language instructions to innovatively derive the functional condition and the spatial condition, where the former specifies the degradation type to remove, while the latter defines its spatial coverage. Then, a composite control priori is generated through a multi-condition coupling network, achieving a seamless transition from abstract language instructions to latent control variables. Subsequently, we design a hybrid attention-based fusion network to aggregate multi-modal information, in which the obtained composite control priori is deeply embedded to linearly modulate the intermediate fused features. To ensure the alignment between language instructions and control outcomes, we introduce a novel language-feature alignment loss, which constrains the consistency between feature-leve

Author

Zhang, Hao; Zha, Yanping; Zhuang, Qingwei; Shao, Zhenfeng; Ma, Jiayi

Published

2025

Language

EN