发表机构
Jilin University; Taiyuan University of Technology; Shenzhen University（吉林大学; 太原理工大学; 深圳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对RGB-红外目标检测的跨模态融合难题，提出以寄存器为核心的RegisterBridgeMM框架，通过三阶段寄存器生命周期实现高效融合，在四个基准数据集上取得最高mAP50-95性能。
AI 中文摘要
RGB-红外（RGB-IR）目标检测得益于可见光与热成像线索的互补性，但在光照变化、天气波动及杂乱场景下，有效融合仍具挑战性。现有RGB-IR融合方法常以牺牲表现力强的补丁级交互为代价，换取更轻量化但约束更强的适配机制。我们通过实验观察到，预训练的寄存器令牌在配对RGB-IR输入中同时包含模态共享与模态专属信息，表明其可作为跨模态通信的紧凑载体。基于该发现，我们提出RegisterBridgeMM，这是一个以三阶段寄存器生命周期组织的寄存器介导融合框架：聚合阶段保留自预训练继承的单模态寄存器摘要；桥接阶段执行双向寄存器到补丁的读取，并引入共识残差正则化；投影阶段将生成的寄存器摘要转换为补丁特征的空间自适应校准。该寄存器通路避免了密集的补丁到补丁跨模态交互，同时保留了预训练的补丁表示。在主干网络均冻结的情况下，RegisterBridgeMM在LLVIP、M3FD、DroneVehicle、FLIR-Aligned四个基准数据集上，取得了所有评估方法中最高的mAP50-95指标。
英文摘要
RGB-infrared (RGB-IR) object detection benefits from complementary visible and thermal cues, but effective fusion remains challenging under illumination changes, weather variation, and cluttered scenes. Existing RGB-IR fusion methods often trade expressive patch-level interaction for lighter but more constrained adaptation mechanisms. We empirically observe that pretrained register tokens contain both modality-shared and modality-specific information on paired RGB-IR inputs, suggesting that they can serve as a compact substrate for cross-modal communication. Building on this observation, we propose RegisterBridgeMM, a register-mediated fusion framework organized as a three-stage register lifecycle. Aggregate preserves per-modality register summarization inherited from pretraining; Bridge performs bidirectional register-to-patch reading with consensus-residual regulation; and Project translates the resulting register summary into spatially adaptive calibration of patch features. This register pathway avoids dense patch-to-patch cross-modal interaction while preserving the pretrained patch representation. With both backbone streams frozen, RegisterBridgeMM achieves the highest mAP50-95 among the evaluated methods on all four benchmarks: LLVIP, M3FD, DroneVehicle, and FLIR-Aligned.
