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Graph-Based Social Relation Reasoning

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12360)

Abstract

Human beings are fundamentally sociable—that we generally organize our social lives in terms of relations with other people. Understanding social relations from an image has great potential for intelligent systems such as social chatbots and personal assistants. In this paper, we propose a simpler, faster, and more accurate method named graph relational reasoning network (GR\(^2\)N) for social relation recognition. Different from existing methods which process all social relations on an image independently, our method considers the paradigm of jointly inferring the relations by constructing a social relation graph. Furthermore, the proposed GR\(^2\)N constructs several virtual relation graphs to explicitly grasp the strong logical constraints among different types of social relations. Experimental results illustrate that our method generates a reasonable and consistent social relation graph and improves the performance in both accuracy and efficiency.

Keywords

Social relation reasoning Paradigm shift Graph neural networks Social relation graph 

Notes

Acknowledgments

This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFA0700802, in part by the National Natural Science Foundation of China under Grant 61822603, Grant U1813218, Grant U1713214, and Grant 61672306, in part by Beijing Natural Science Foundation under Grant No. L172051, in part by Beijing Academy of Artificial Intelligence (BAAI), in part by a grant from the Institute for Guo Qiang, Tsinghua University, in part by the Shenzhen Fundamental Research Fund (Subject Arrangement) under Grant JCYJ20170412170602564, and in part by Tsinghua University Initiative Scientific Research Program.

Supplementary material

504470_1_En_2_MOESM1_ESM.pdf (405 kb)
Supplementary material 1 (pdf 404 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of AutomationTsinghua UniversityBeijingChina
  2. 2.State Key Lab of Intelligent Technologies and SystemsBeijingChina
  3. 3.Beijing National Research Center for Information Science and TechnologyBeijingChina
  4. 4.Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityBeijingChina
  5. 5.Stanford UniversityStanfordUSA

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