TL;DR
This paper introduces CLDyN, a novel adaptive image fusion network that dynamically adjusts to multiple downstream tasks through a closed-loop mechanism and semantic compensation, enhancing multi-task performance.
Contribution
The paper proposes a closed-loop optimization framework with semantic modules that enable task-specific adaptation in infrared-visible image fusion without retraining.
Findings
CLDyN achieves high-quality fusion across diverse datasets.
The method demonstrates strong adaptability to multiple downstream tasks.
Experimental results outperform existing fusion approaches.
Abstract
Infrared-visible image fusion aims to integrate complementary information for robust visual understanding, but existing fusion methods struggle with simultaneously adapting to multiple downstream tasks. To address this issue, we propose a Closed-Loop Dynamic Network (CLDyN) that can adaptively respond to the semantic requirements of diverse downstream tasks for task-customized image fusion. Specifically, CLDyN introduces a closed-loop optimization mechanism that establishes a semantic transmission chain to achieve explicit feedback from downstream tasks to the fusion network through a Requirement-driven Semantic Compensation (RSC) module. The RSC module leverages a Basis Vector Bank (BVB) and an Architecture-Adaptive Semantic Injection (A2SI) block to customize the network architecture according to task requirements, thereby enabling task-specific semantic compensation and allowing the…
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