Co-saliency detection aims to detect common salient objects from a group of
relevant images. Some attempts have been made with the Fully Convolutional
Network (FCN) framework and achieve satisfactory detection results. However,
due to stacking convolution layers and pooling operation, the boundary details
tend to be lost. In addition, existing models often utilize the extracted
features without discrimination, leading to redundancy in representation since
actually not all features are helpful to the final prediction and some even
bring distraction. In this paper, we propose a co-attention module embedded FCN
framework, called as Co-Attention FCN (CA-FCN). Specifically, the co-attention
module is plugged into the high-level convolution layers of FCN, which can
assign larger attention weights on the common salient objects and smaller ones
on the background and uncommon distractors to boost final detection
performance. Extensive experiments on three popular co-saliency benchmark
datasets demonstrate the superiority of the proposed CA-FCN, which outperforms
state-of-the-arts in most cases. Besides, the effectiveness of our new
co-attention module is also validated with ablation studies.

本文提出了嵌入了共同注意力模块的全卷积网络框架，称为共同注意力 FCN (CA-FCN)，旨在检测一组相关图像中的共同显著对象。实验表明，CA-FCN 在大多数情况下优于现有算法，同时通过消融研究证明了新共同注意力模块的有效性。