Contour-Aware Loss: Boundary-Aware Learning for Salient Object Segmentation

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 salient object segmentation. Yet the Contour Loss emphasizes the boundaries to capture the contextual details in the local range. We further propose the hierarchical global attention module (HGAM), which forces the model hierarchically to 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.

Automated summary

The learning model utilizes boundary information for salient object segmentation, with a novel Contour Loss function guiding the perception of object boundaries, enhancing the segmentation effectiveness. Experimental results demonstrate superior performance, with real-time speed achieved on a TITAN X GPU.