  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.
