Abstract. We propose a novel scene graph generation model called
Graph R-CNN, that is both effective and efficient at detecting objects
and their relations in images. Our model contains a Relation Proposal
Network (RePN) that efficiently deals with the quadratic number of potential relations between objects in an image. We also propose an attentional Graph Convolutional Network (aGCN) that effectively captures
contextual information between objects and relations. Finally, we introduce a new evaluation metric that is more holistic and realistic than
existing metrics. We report state-of-the-art performance on scene graph
generation as evaluated using both existing and our proposed metrics