Given that existing salient object detection methods cannot effectively predict the fine contours of salient objects when extracting local or global contexts and features, we propose a novel contour self-compensated network (CSCNet) to generate a more accurate saliency map with complete contour. Unlike the common binary saliency detection, we reconstruct the salient object detection problem into a multi-classification problem of the background, the salient object, and the salient contour, where the salient contour is used as the third label for ground truth. Meanwhile, the image and its superpixel map are concatenated as the input of our network to add more edge information. Also, a penalty loss is proposed to restrict the spatial relationship between the background, objects, and their contours. Experimentally, we evaluate the proposed CSCNet on six benchmark datasets in both accuracy and efficiency and evaluate the attribute-based performance on the SOC dataset. Compared with 13 state-of-the-art algorithms, our CSCNet can detect salient objects more accurately and completely without adding too many convolutional layers and parameters.

中文翻译：

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用于显着目标检测的轮廓自补偿网络

鉴于现有的显着对象检测方法在提取局部或全局上下文和特征时无法有效预测显着对象的精细轮廓，我们提出了一种新颖的轮廓自补偿网络（CSCNet）来生成更准确的具有完整轮廓的显着图。与常见的二元显着性检测不同，我们将显着对象检测问题重构为背景、显着对象和显着轮廓的多分类问题，其中显着轮廓用作ground truth的第三个标签。同时，将图像及其超像素图作为我们网络的输入进行连接，以添加更多边缘信息。此外，还提出了惩罚损失来限制背景、对象及其轮廓之间的空间关系。实验上，我们在六个基准数据集上评估了提议的 CSCNet 的准确性和效率，并评估了 SOC 数据集上基于属性的性能。与 13 种最先进的算法相比，我们的 CSCNet 可以更准确、更完整地检测显着对象，而无需添加太多卷积层和参数。