DH-GCN: Saliency-Aware Complex Scene Graph Generation Using Dual-Hierarchy Graph Convolutional Network

In reality, complex scene plagues numerous scene graph generation models because realistic scene contains myriad of objects and complicated relationships. Most current methods suffer poor performance when encountering complex scenes. We find that there are two principal reasons for this phenomenon. First, the construction of graph loses sight of the hierarchy of objects. Second, there exists redundant information in feature optimization. To facilitate this issue, this paper proposes an innovative dual-hierarchy graph convolutional network (DH-GCN), which is a conceptually elegant and efficient top-down approach. In specific, DH-GCN leverages salient object detector to hierarchize objects and give gist nodes more accurate representation. Moreover, the dual-hierarchy message propagation is designed to refine the representation hierarchically and eliminate redundant information. Systematic experiments on Visual Genome dataset show the superiority of our method over strong baseline methods.