The purpose of co-saliency detection is to find out the salient and common objects of related images. This paper proposes a novel reverse collaborative fusion model (RCFM) for co-saliency detection. The model is mainly composed of two parts: reverse message fusion module (RMFM) and collaborative consistency learning module (CCLM). Specifically, we first aggregate the features in high-level layers as global guidance by using the cascaded decoder (CD). Then, we propose repeated RMFMs on each side output to complete the complementary fusion of deep and shallow information. Then, we fuse multi-scale feature maps as initial co-saliency maps. Finally, the CCLM extracts the collaborative information between images to improve the quality of the initial co-saliency map to obtain the final co-saliency map. The model fully considers the semantic features of high-level and the boundary features of low-level, thereby correcting some deviation predictions and improving the accuracy of co-saliency detection. Compared to the state-of-the-art approaches, experimental results demonstrate that our proposed approach achieves the best performance on four evaluation indicators of three datasets.

共显着性检测的目的是找出相关图像的显着和共同对象。本文提出了一种用于共显着性检测的新型反向协同融合模型（RCFM）。该模型主要由两部分组成：反向消息融合模块（RMFM）和协同一致性学习模块（CCLM）。具体来说，我们首先使用级联解码器 (CD) 将高级层中的特征聚合为全局指导。然后，我们在每侧输出上提出重复的 RMFM，以完成深浅信息的互补融合。然后，我们将多尺度特征图融合为初始共显着图。最后，CCLM提取图像之间的协同信息，提高初始共显着图的质量，得到最终的共显着图。该模型充分考虑了高层的语义特征和低层的边界特征，从而纠正了一些偏差预测，提高了共显着性检测的准确性。与最先进的方法相比，实验结果表明，我们提出的方法在三个数据集的四个评估指标上取得了最佳性能。