  We present a learning model that makes full use of boundary information for
salient object segmentation. Specifically, we come up with a novel loss
function, i.e., Contour Loss, which leverages object contours to guide models
to perceive salient object boundaries. Such a boundary-aware network can learn
boundary-wise distinctions between salient objects and background, hence
effectively facilitating the saliency detection. Yet the Contour Loss
emphasizes on the local saliency. We further propose the hierarchical global
attention module (HGAM), which forces the model hierarchically attend to global
contexts, thus captures the global visual saliency. Comprehensive experiments
on six benchmark datasets show that our method achieves superior performance
over state-of-the-art ones. Moreover, our model has a real-time speed of 26 fps
on a TITAN X GPU.
