Difference-Guided Modality Fusion Network for Multimodal Object Detection

In recent years, visible-infrared object detection has achieved significant progress. However, most existing methods primarily emphasize the shared features between the two modalities while overlooking their feature differences. To address this limitation, we propose the Difference-Guided Modality Fusion Network, which can effectively improve the fusion and detection performance of modalities. Specifically, we propose a cross-modal data augmentation strategy to overcome the limitations of single-modality reliance by exchanging the partial modal information. To further capture and analyze feature differences between modalities, we introduce a differential attention fusion approach that models a difference matrix across modal channels, thereby quantifying and strengthening the salient features of the two modalities. Additionally, we develop a modality-aware dynamic learning mechanism that employs a loss function that can simultaneously focus on the differences and common parts of the modalities, guiding the model to adaptively learn features between the modalities. Experimental results on FLIR, LLVIP and M³FD datasets demonstrate the effectiveness of the proposed method, with mAP reaching 42.3%, 67.5% and 59.0% respectively.