Multi-modal cooperative fusion network for dual-stream RGB-D salient object detection

Abstract

Most existing RGB-D salient object detection tasks use convolution operations to design complex fusion modules for cross-modal information fusion. How to correctly integrate RGB and depth features into multi-modal features is important to salient object detection (SOD). Due to the differences between different modal features, the salient object detection model is seriously hindered in achieving better performance. To address the issues mentioned above, we design a multi-modal cooperative fusion network (MCFNet) to achieve RGB-D SOD. Firstly, we propose an edge feature refinement module to remove interference information in shallow features and improve the edge accuracy of SOD. Secondly, a depth optimization module is designed to optimize erroneous estimates in the depth maps, which effectively reduces the impact of noise and improves the performance of the model. Finally, we construct a progressive fusion module that gradually integrates RGB and depth features in a layered manner to achieve an efficient fusion of cross-modal features. Experimental results on six datasets show that our MCFNet performs better than other state-of-the-art (SOTA) methods, which provide new ideas for salient object detection tasks.