  Multispectral object detection, utilizing both visible (RGB) and thermal
infrared (T) modals, has garnered significant attention for its robust
performance across diverse weather and lighting conditions. However,
effectively exploiting the complementarity between RGB-T modals while
maintaining efficiency remains a critical challenge. In this paper, a very
simple Group Shuffled Multi-receptive Attention (GSMA) module is proposed to
extract and combine multi-scale RGB and thermal features. Then, the extracted
multi-modal features are directly integrated with a multi-level path
aggregation neck, which significantly improves the fusion effect and
efficiency. Meanwhile, multi-modal object detection often adopts union
annotations for both modals. This kind of supervision is not sufficient and
unfair, since objects observed in one modal may not be seen in the other modal.
To solve this issue, Multi-modal Supervision (MS) is proposed to sufficiently
supervise RGB-T object detection. Comprehensive experiments on two challenging
benchmarks, KAIST and DroneVehicle, demonstrate the proposed model achieves the
state-of-the-art accuracy while maintaining competitive efficiency.
