Primary-Complementary Bidirectional Enhancement and Query-wise Modality Preference Selection for Visible-Infrared Object Detection

Existing visible-infrared object detectors commonly fuse heterogeneous features into a single representation for detection. However, this paradigm may weaken modality-specific discriminative cues and fail to explicitly model object-level modality preferences. To address these challenges, we propose BEPS-Net, a query-based visible-infrared object detection framework. Specifically, we introduce Primary-Complementary Bidirectional Enhancement (PCBE), which treats each modality as the primary representation in one enhancement direction and incorporates complementary information from the other. This design maintains distinct visible-primary and infrared-primary representations, helping preserve modality-specific discriminative cues while exploiting cross-modal complementarity. Furthermore, we introduce Query-wise Modality Preference Selection (QMPS), which integrates query initialization from both representations with adaptive modality routing, allowing each object query to dynamically reassess its modality preference at every decoder layer and select informative cues from the two enhanced representations. Extensive experiments demonstrate that BEPS-Net outperforms existing state-of-the-art visible-infrared detectors on multiple benchmarks, while the proposed designs can be effectively extended to visible-infrared semantic segmentation. The code is available at: https://anonymous.4open.science/r/BEPSNet.