Joint learning of foreground, background and edge for salient object detection

Although significant progress has been made in saliency detection, predicting saliency remains challenging when the scene is complex, especially when salient and non-salient regions are similar or salient objects have intricate contours. Previous advanced methods rarely explored learning in the background of images. In fact, background and foreground of an image contain complementary information. In this work, we propose to decompose the saliency detection task into three subtasks: foreground awareness, background suppression, and edge refinement. More specifically, our decoder is comprised of three branches: a foreground awareness branch, a background suppression branch, and an edge refinement branch. Each branch aims to learn specific features for predicting its corresponding map. Meanwhile, we design a regional focus loss function with controllable modulating factors to supervise the learning of each branch at the training stage. Moreover, we build an attention guided feature fusion module to adaptively fuse multi-scale features and a global information capture module to locate salient objects. Experiments on five benchmark datasets demonstrate that our approach is superior to the state-of-the-art methods.