Cross-Stage Multi-Scale Interaction Network for RGB-D Salient Object Detection

Salient object detection (SOD) aims to detect the most prominent objects and regions in the human vision. Since the RGB and depth modalities contain discrepant characteristics and convey the clues of different domains, how to explore the fusion of multi-modal information and the interaction of cross-stage features remain the key problems in RGB-D SOD. In this letter, we propose a cross-stage multi-scale interaction network (CMINet), consisting of a multi-scale spatial pooling (MSP) module and a cross-stage pyramid interaction (CPI) module to interweave the feature maps of different stages in a bottom-up and top-down way. In addition, we also design an adaptive weight fusion (AWF) module to weigh the importance of multimodality features and fuse them. Extensive experiments are conducted on 4 widely used datasets to validate the effectiveness of the proposed CMINet. The results demonstrate that our approach achieves state-of-the-art performance against other 11 methods under 4 evaluation metrics.