Title: Cross-Modal Feature Disentanglement with Contrastive Task Alignment for Multi-Modal Image Fusion
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Multi-modal image fusion, Feature disentanglement, Orthogonal feature decomposition, Gram–Schmidt reparameterization, Multi-granularity contrastive learning, Cross-task generalization

Abstract: Multi-modal image fusion suffers from feature entanglement, where modality-specific, content-specific, and task-specific information becomes conflated in unified representation spaces, leading to suboptimal fusion quality and limited generalization. This paper proposes Cross-Modal Feature Disentanglement with Contrastive Task Alignment (CMD-CTA), a principled framework that addresses this fundamental challenge through mathematically grounded feature separation and semantic alignment. The approach introduces two key innovations: (1) differentiable orthogonal feature decomposition that enforces separation into content, modality, and task subspaces with theoretical guarantees for mutual information minimization; and (2) contrastive task alignment that establishes semantic bridges through learnable prototypes and multi-granularity learning. Extensive experiments across six fusion tasks demonstrate consistent improvements of 5.8-7.3\% over state-of-the-art methods while achieving 15.7× parameter efficiency. On downstream object detection, CMD-CTA achieves 96.8\% mAP\@0.5, surpassing previous results by 6.1\%. This work establishes a new paradigm for representation learning in multi-modal fusion with broad implications for computer vision and autonomous systems.