Multiple Graph Convolutional Networks for Co-Saliency Detection
Bo Jiang, Xingyue Jiang, Jin Tang, Bin Luo, Shilei Huang · 2019
Recently, Graph Convolutional Networks (GCNs) have been usually utilized for graph data representation in computer vision area. However, existing graph GCNs generally use a single graph which can be not adapted for the data with multiple graphs. In this paper, we first propose a novel multiple graph convolutional network (MGCN) for multiple graph data representation and learning. MGCN propagates information/knowledge across multiple graphs and obtains a consistent representation and learning by integrating the information of multiple graphs simultaneously. Based on the proposed MGCN, we then propose a new global-local unified graph convolutional learning architecture for image co-saliency detection problem. The main benefits of the proposed co-saliency model are twofold. First, it learns an optimal superpixel feature representation for co-saliency detection problem. Second, it can well exploit both intra-image and inter-image cues for co-saliency detection via a unified network. Promising experiments demonstrate the effectiveness of the proposed MGCN based co-saliency detection approach.