超越对偶性：利用共享与私有特征的混合框架用于RGB-事件目标检测
Beyond Duality: A Hybrid Framework of Leveraging Shared and Private Features for RGB-Event Object Detection
提出基于频域一致性的共享与私有特征解耦方法，可复用于其他多模态融合检测任务；TriAdapt 编码器与 TriInject 解码器的设计思路对处理模态间互补信息有参考价值。
原文依据 we propose a frequency-domain coherence-based Shared and Private Features Decoupling method for RGB-Event object detection, SPFD network.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论RGB-事件目标检测能够捕获目标的清晰细节特征，同时保持高速信息采集。它适用于高动态或恶劣环境，近年来已成为研究热点。现有的RGB-事件目标检测器都难以充分利用两种模态的融合特征，且未显式地将共享特征与私有特征解耦到专用分支。为充分挖掘单一特征的潜力，我们提出了一种基于频域一致性的共享与私有特征解耦方法用于RGB-事件目标检测，即SPFD网络。首先，我们设计了一个FCFS模块，通过探索双模态间的光谱能量分布差异来分离共享特征与私有特征。然后，我们设计了一个TriAdapt编码器来处理共享与私有特征，在静态区域选择性强调纹理丰富的RGB特征，在动态区域强调运动敏感的事件特征，从而在空间细节与时序感知之间实现稳健平衡。最后，提出了一个TriInject解码器来动态强调最具判别性的模态特征。在DSEC-Det和PKU-DAVIS-SOD数据集上的实验结果表明，我们的模型达到了与最优方法相竞争的性能。
英文原文摘要 1332 字符
RGB-Event object detection is able to capture clear and detailed features of the target while maintaining high-speed information collection. It is suitable for high dynamic or harsh environments and has become a research hotspot in recent years. The existing RGB-Event object detectors all struggle to fully utilize the fusion features of two modalities, but do not explicitly disentangle shared vs. private features to dedicated branches. To fully tap into the potential of single features, we propose a frequency-domain coherence-based Shared and Private Features Decoupling method for RGB-Event object detection, SPFD network. First, we design a FCFS module to separate shared and private features by exploring the spectral energy distribution differences between dual modalities. Then, we design a TriAdapt Encoder to process the shared and private features, selectively emphasizing texture-rich RGB features in static regions and motion-sensitive event features in dynamic regions, thereby achieving a robust balance between spatial detail and temporal awareness. Finally, a TriInject Decoder is proposed to emphasize the most discriminative modality features dynamically. Experimental results on the DSEC-Det and PKU-DAVIS-SOD datasets demonstrate that our model achieves competitive performance with state-of-the-art methods.