From 2D Grids to 1D Tokens: Reforming Shared Representations for Multimodal Image Fusion
This work addresses the trade-off between local structure and global appearance in multimodal image fusion, offering a lightweight method that improves both aspects without modifying the fusion backbone.
The paper introduces a 1D token interface for multimodal image fusion that balances local detail preservation and global appearance coherence, achieving state-of-the-art performance across four benchmarks with consistent improvements in both global and local metrics.
Multimodal image fusion aims to integrate complementary information from different modalities into a fused image that preserves rich local details while maintaining globally consistent appearance. Existing approaches build shared representations on 2D feature grids, which excel at modeling local structures but offer limited leverage over image-level global appearance factors. To balance these objectives, we introduce a compact 1D token interface based on a frozen pretrained image tokenizer for modeling non-local appearance/base factors. Rather than using the tokenizer as a reconstruction backbone, our design uses the 1D token space as a global carrier while retaining the 2D spatial pathway for local structure restoration. Specifically, we introduce Selective Token Editing (STE), which sparsely updates/replaces a small set of critical tokens, providing a lightweight mechanism to steer global appearance coherence while keeping the fusion backbone unchanged and avoiding extra losses. Experiments on four commonly used benchmarks show that our method achieves the best overall performance, with consistent, multi-metric improvements in both global coherence and local fidelity. Project page: https://zju-xyc.github.io/1D-Fusion-Project-Page/