Beyond Duality: A Hybrid Framework of Leveraging Shared and Private Features for RGB-Event Object Detection

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. Our codes are available at https://github.com/git-KeYw/SPFD.