In RGB-D object detection, due to the inherent difference between the RGB and Depth modalities, it remains challenging to simultaneously leverage sensed photometric and depth information. In this paper, to address this issue, we propose a Feature Exchange Transformer Network (FETNet), which consists of two well-designed components: the Feature Exchange Module (FEM), and the Multi-modal Vision Transformer (MViT). Specially, we propose the FEM to exchange part of the channels between RGB and

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... features at each backbone stage, which facilitates the information flow, and bridges the gap, between the two modalities. Inspired by the success of Vision Transformer (ViT), we develop the variant MViT to effectively fuse multi-modal features and exploit the attention between the RGB and depth features. Different from previous methods developing from specified RGB detection algorithm, our proposal is generic. Extensive experiments prove that, when the proposed modules are integrated into mainstream RGB object detection methods, their RGB-D counterparts can obtain significant performance gains. Moreover, our FETNet surpasses state-of-the-art RGB-D detectors by 7.0% mAP on SUN RGB-D and 1.7% mAP on NYU Depth v2, which also well demonstrates the effectiveness of the proposed method.