Boundary-Aligned Contribution Routing for Robust Optical--SAR Object Detection
This study addresses the negative transfer issue arising from imperfect modal correspondence in optical-SAR fusion detection by proposing a Boundary-Aligned Contribution Routing mechanism. Leveraging feature-level and dual-statistic semantic routers, this method dynamically modulates modal contributions prior to feature mixing without requiring additional utility supervision, thereby effectively suppressing negative transfer. Experimental results demonstrate that the proposed mechanism significantly enhances fusion robustness, improving mean Average Precision (mAP) by 0.5–5.9 points under full-input conditions and 7.6–41.6 points in missing-modality scenarios. Furthermore, it reduces the negative transfer rate by up to 12.7%, successfully mitigating performance degradation challenges inherent in heterogeneous multimodal fusion tasks.