Task-Customized Mixture of Adapters for General Image Fusion

• URL: http://arxiv.org/abs/2403.12494v2
• Date: Sun, 24 Mar 2024 03:17:24 GMT
• Title: Task-Customized Mixture of Adapters for General Image Fusion
• Authors: Pengfei Zhu, Yang Sun, Bing Cao, Qinghua Hu,
• Abstract summary: General image fusion aims at integrating important information from multi-source images. We propose a novel task-customized mixture of adapters (TC-MoA) for general image fusion, adaptively prompting various fusion tasks in a unified model.
• Score: 51.8742437521891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: General image fusion aims at integrating important information from multi-source images. However, due to the significant cross-task gap, the respective fusion mechanism varies considerably in practice, resulting in limited performance across subtasks. To handle this problem, we propose a novel task-customized mixture of adapters (TC-MoA) for general image fusion, adaptively prompting various fusion tasks in a unified model. We borrow the insight from the mixture of experts (MoE), taking the experts as efficient tuning adapters to prompt a pre-trained foundation model. These adapters are shared across different tasks and constrained by mutual information regularization, ensuring compatibility with different tasks while complementarity for multi-source images. The task-specific routing networks customize these adapters to extract task-specific information from different sources with dynamic dominant intensity, performing adaptive visual feature prompt fusion. Notably, our TC-MoA controls the dominant intensity bias for different fusion tasks, successfully unifying multiple fusion tasks in a single model. Extensive experiments show that TC-MoA outperforms the competing approaches in learning commonalities while retaining compatibility for general image fusion (multi-modal, multi-exposure, and multi-focus), and also demonstrating striking controllability on more generalization experiments. The code is available at https://github.com/YangSun22/TC-MoA .

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