Multispectral Pedestrian Detection via Reference Box Constrained Cross Attention and Modality Balanced Optimization

Multispectral pedestrian detection is an important task for many around-the-clock applications, since the visible and thermal modalities can provide complementary information especially under low light conditions. To reduce the influence of hand-designed components in available multispectral pedestrian detectors, we propose a MultiSpectral pedestrian DEtection TRansformer (MS-DETR), which extends deformable DETR to multi-modal paradigm. In order to facilitate the multi-modal learning process, a Reference box Constrained Cross-Attention (RCCA) module is firstly introduced to the multi-modal Transformer decoder, which takes fusion branch together with the reference boxes as intermediaries to enable the interaction of visible and thermal modalities. To further balance the contribution of different modalities, we design a modality-balanced optimization strategy, which aligns the slots of decoders by adaptively adjusting the instance-level weight of three branches. Our end-to-end MS-DETR shows superior performance on the challenging KAIST and CVC-14 benchmark datasets.