提出边界对齐路由机制，让光学与雷达图像融合更稳定高效。
Boundary-Aligned Contribution Routing for Robust Optical--SAR Object Detection
- 通过检测监督学习动态路由，按任务需求分配模态贡献
- 在多种干扰下提升检测精度，最高增益达5.9点
- 无需额外标签或训练，适合遥感目标检测场景
光学图像提供丰富的外观信息，而合成孔径雷达（SAR）对光照和天气不敏感，使得光学与SAR融合在遥感目标检测中具有吸引力。然而，多模态存在并不保证融合有益：空间、时间与语义对应不一致可能使原本完整的数据流在特定条件下有害，引发负向跨模态迁移。本文从模型特异性任务效用视角出发，仅使用检测监督学习任务条件化的贡献路由。提出的融合边界对齐路由在首次跨模态特征值混合前调节各模态贡献。针对频繁浅层交互架构，采用输入附近可跨条件组地址调制的特征路由器；对于双主干架构，使用双统计语义路由器，基于模态特异的均值与最大值统计预测流级贡献权重，再进行晚期语义融合。路由器无需显式效用监督、质量标签、重建或知识蒸馏。在M4-SAR和SpaceNet6-OTD数据集上，涵盖完整输入、可控对应偏移、缺失模态及四种非零模态扰动场景的实验表明，相比基准方法，路由在干净训练条件下将全输入mAP₅₀提升0.5–5.9点；在缺失模态情况下，提升7.6–41.6点，并将负向迁移率降低最多12.7个百分点。所学路由权重与模型特异性“留一模态”效用的斯皮尔曼相关系数为0.45–0.66，支持路由系数的任务效用解释。
原文摘要 · Abstract (English)
Optical imagery provides rich appearance cues, whereas synthetic aperture radar (SAR) offers observations that are less sensitive to illumination and weather, making optical--SAR fusion attractive for remote-sensing object detection. However, the presence of multiple modalities does not guarantee beneficial fusion: imperfect spatial, temporal, and semantic correspondence can make an otherwise intact stream conditionally harmful and induce negative cross-modal transfer. We handle this issue through a model-specific task-utility perspective and learn task-conditioned contribution routing using detection supervision alone. The proposed fusion-boundary-aligned routing regulates each modality's contribution before the first learned cross-modal feature-value mixing operation. For architectures with frequent shallow interaction, a Feature Router performs cross-conditioned, group-addressable modulation near the input; for dual-backbone architectures, a Dual-Statistic Semantic Router predicts stream-level contribution weights from modality-specific average and maximum statistics before late semantic fusion. The routers require no explicit utility supervision, quality labels, reconstruction, or distillation. Experiments on M4-SAR and SpaceNet6-OTD cover nominal full inputs, controlled correspondence shifts, missing modalities, and four nonzero modality-corruption scenarios. Across the reported clean-training controls, routing improves full-input $\text{mAP}_{50}$ by 0.5--5.9 points. Relative to the corresponding modality-dropout baselines, it raises missing-modality $\text{mAP}_{50}$ by 7.6--41.6 points and reduces the negative-transfer rate by up to 12.7 percentage points. Spearman correlations between the learned routing weights and model-specific leave-one-modality-out utility range from 0.45 to 0.66, supporting the task-utility interpretation of the routing coefficients.
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