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.

中文翻译：

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轮廓感知损失：用于显着对象分割的边界感知学习

我们提出了一种学习模型，该模型充分利用边界信息进行显着对象分割。具体而言，我们提出了一种新颖的损失函数，即轮廓损失，该函数利用对象轮廓来指导模型来感知显着的对象边界。这样的边界感知网络可以学习显着对象和背景之间的边界区分，从而有效地促进了显着对象的分割。然而，轮廓损失强调边界以捕获本地范围内的上下文细节。我们进一步提出了分层的全球注意力模块（HGAM），该模块强制模型分层地参与全局上下文，从而捕获全局视觉显着性。在六个基准数据集上进行的综合实验表明，我们的方法比最先进的方法具有更高的性能。此外，