A pooling-based feature pyramid network for salient object detection

Abstract

How to effectively utilize and fuse deep features has become a critical point for salient object detection. Most existing methods usually adopt the convolutional features based on U-shape structures and fuse multi-scale convolutional features without fully considering the different characteristics between high-level features and low-level features. Furthermore, existing salient object detection methods rarely consider the role of pooling in convolutional neural networks. Moreover, there is still much room to improve the detection performance for objects in complex scenes. To address the problems mentioned above, we propose a pooling-based feature pyramid (PFP) network to boost salient object detection performance in this paper. First, we design two U-shaped feature pyramid modules to capture rich semantic information from high-level features and to obtain clear saliency boundaries from low-level features respectively. Second, a pyramid pooling refinement module is designed to utilize the pooling to capture more semantic information. Third, a universal channel-wise attention (UCA) module is designed to select effective high-level features of multi-scale and multi-receptive-field for rich semantic information, even in complex scenes. Finally, we fuse the selected high-level features and low-level features together, followed by an edge preservation loss to obtain accurate boundary location. Extensive experiments are conducted on five datasets and the experimental results indicate that our proposed method has the ability to get better salient object detection performance compared to the state-of-the-art methods.