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.

提出了一种新颖的场景图生成模型 ——Graph R-CNN，其在检测图像中的对象和它们之间的关系方面既有效又高效。该模型包含一种关系提议网络（RePN），能够有效地处理图像中潜在关系的二次数量。同时，我们提出了一种注意力图卷积网络（aGCN），有效地捕捉对象和关系之间的上下文信息。最后，我们引入了一种比现有指标更全面、更实际的评估方法。我们使用现有指标和我们提出的评估方法评估场景图生成，获得了最先进的性能。