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Graph R-CNN for Scene Graph Generation

  • Jianwei YangEmail author
  • Jiasen Lu
  • Stefan Lee
  • Dhruv Batra
  • Devi Parikh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11205)

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.

Keywords

Graph R-CNN Scene graph generation Relation proposal network Attentional graph convolutional network 

Notes

Acknowledgements

This work was supported in part by NSF, AFRL, DARPA, Siemens, Google, Amazon, ONR YIPs and ONR Grants N00014-16-1-{2713,2793}.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.Facebook AI ResearchMenlo ParkUSA

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