DYNAMIC SELECTION NETWORK FOR RGB-D SALIENT OBJECT DETECTION

Existing RGB-D salient object detection (SOD) methods usually use elaborate fusion modules for exploring cross-modal information, which is computationally expensive and ignores the noise depth information. To deal with this issue, we propose a dynamic selection network (DSNet) for RGB-D salient object detection. Specifically, a cross-modal combination module (CCM) is proposed to fuse two modalities with a light computation. Then a dynamic selection module (DSM) adaptively learns the model parameter for the decoding based on the fused features. Furthermore, skip connection is used for hierarchical features combination between encoder and decoder. Experiments on four popular datasets demonstrate our model outperforms other state-of-the-art methods.