Deep convolutional neural networks have been widely applied in salient object detection and have achieved remarkable results in this field. However, existing models suffer from information distortion caused by interpolation during up-sampling and down-sampling. In response to this drawback, this article starts from two directions in the network: feature and label. On the one hand, a novel cascaded interaction network with a guidance module named global-local aligned attention (GAA) is designed to reduce the negative impact of interpolation on the feature side. On the other hand, a deep supervision strategy based on edge erosion is proposed to reduce the negative guidance of label interpolation on lateral output. Extensive experiments on five popular datasets demonstrate the superiority of our method.

翻译：深度卷积神经网络已广泛应用于显著目标检测，并在该领域取得了显著成果。然而，现有模型在上下采样过程中因插值操作导致信息失真。针对这一缺陷，本文从网络特征与标签两个方向入手：一方面，设计了一种新型级联交互网络，引入名为全局局部对齐注意力（GAA）的引导模块，以降低插值对特征侧的负面影响；另一方面，提出了一种基于边缘侵蚀的深度监督策略，以减少标签插值对侧输出的错误引导。在五个公开数据集上的大量实验表明，本方法具有优越性。