Multi-branch feature fusion and refinement network for salient object detection

Jinyu Yang, Yanjiao Shi, Jin Zhang, Qianqian Guo, Qing Zhang, Liu Cui

Abstract: With the development of convolutional neural networks (CNNs), salient object detection methods have made great progress in performance. Most methods are designed with complex structures to aggregate the multi-level feature maps, to reach the goal of filtering noise and obtaining rich information. However, there is no differentiation when dealing with the multi-level features, and only a uniform treatment is used in general. Based on the above considerations, in this paper, we propose a multi-branch feature fusion and refinement network (MFFRNet), which is a framework for treating low-level features and high-level features differently, and effectively fuses the information of multi-level features to make the results more accurate. We propose a detail optimization module (DOM) designed for the rich detail information in low-level features and a pyramid feature extraction module (PFEM) designed for the rich semantic information in high-level features, as well as a feature optimization module (FOM) for refining the fused feature of multiple levels. Extensive experiments are conducted on six benchmark datasets, and the results show that our approach outperforms the state-of-the-art methods.