  In this paper, we propose an end-to-end group-wise deep co-saliency detection
approach to address the co-salient object discovery problem based on the fully
convolutional network (FCN) with group input and group output. The proposed
approach captures the group-wise interaction information for group images by
learning a semantics-aware image representation based on a convolutional neural
network, which adaptively learns the group-wise features for co-saliency
detection. Furthermore, the proposed approach discovers the collaborative and
interactive relationships between group-wise feature representation and
single-image individual feature representation, and model this in a
collaborative learning framework. Finally, we set up a unified end-to-end deep
learning scheme to jointly optimize the process of group-wise feature
representation learning and the collaborative learning, leading to more
reliable and robust co-saliency detection results. Experimental results
demonstrate the effectiveness of our approach in comparison with the
state-of-the-art approaches.
