Abstract
The main focus of existing RGB-D salient object detection models is achieving effective multi-modal fusion. Due to the limited receptive field of conventional convolutional neural networks (CNNs), CNN-based multi-modal fusion strategies fail to extensively model the correlation between the two modalities (appearance information from the RGB image and geometric information from the depth data). Given the success of transformer networks for long-range dependency modeling, we investigate multi-modal transformer networks for RGB-D salient object detection. Specifically, a transformer-based multi-modal fusion module is presented to effectively fuse appearance features and geometric features. Experimental results on six challenging benchmark RGB-D salient object detection datasets demonstrate the effectiveness of our approach.
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