Co-salient object detection aims to find common salient objects from an image group, which is a branch of salient object detection. This paper proposes a global-guided cross-reference network. The cross-reference module is designed to enhance the multi-level features from two perspectives. From the spatial perspective, the location information of objects with similar appearances must be highlighted. From the channel perspective, more attention must be assigned to channels that indicate the same object category. After spatial and channel cross-reference, the features are enhanced to possess the consensus representation of image group. Next, a global co-semantic guidance module is built to provide hierarchical features with the location information of co-salient objects. Compared with state-of-the-art co-salient object detection methods, our proposed method extracts collaborative information and obtains better co-saliency maps on several challenging co-saliency detection datasets.

共显着对象检测旨在从图像组中找到共同的显着对象，是显着对象检测的一个分支。本文提出了一个全局引导的交叉引用网络。交叉引用模块旨在从两个角度增强多级特征。从空间的角度来看，必须突出具有相似外观的物体的位置信息。从通道的角度来看，必须将更多的注意力分配给指示相同对象类别的通道。经过空间和通道的交叉引用，特征被增强以拥有图像组的一致表示。接下来，构建一个全局协同语义引导模块，为分层特征提供协同显着对象的位置信息。与最先进的共显着目标检测方法相比，