One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion
Journal:
arXiv
Published Date:
Feb 27, 2025
Abstract
Advanced image fusion methods mostly prioritise high-level missions, where
task interaction struggles with semantic gaps, requiring complex bridging
mechanisms. In contrast, we propose to leverage low-level vision tasks from
digital photography fusion, allowing for effective feature interaction through
pixel-level supervision. This new paradigm provides strong guidance for
unsupervised multimodal fusion without relying on abstract semantics, enhancing
task-shared feature learning for broader applicability. Owning to the hybrid
image features and enhanced universal representations, the proposed GIFNet
supports diverse fusion tasks, achieving high performance across both seen and
unseen scenarios with a single model. Uniquely, experimental results reveal
that our framework also supports single-modality enhancement, offering superior
flexibility for practical applications. Our code will be available at
https://github.com/AWCXV/GIFNet.
