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Social relationship understanding infers existing social relationships among individuals in a given scenario, which has been demonstrated to have a wide range of practical value in reality. However, existing methods infer the social relationship of each person pair in isolation, without considering the context-aware information for person pairs in the same scenario. The context-aware information for person pairs exists extensively in reality, that is, the social relationships of different person pairs in a simple scenario are always related to each other. For instance, if most of the person pairs in a simple scenario have the same social relationship, "friends", then the other pairs have a high probability of being "friends" or other similar coarse-level relationships, such as "intimate" . This context-aware information should thus be considered in social relationship understanding. Therefore, this paper proposes a novel end-to-end trainable Person-Pair Relation Network (PPRN), which is a GRU-based graph inference network, to first extract the visual and position information as the person-pair feature information, then enable it to transfer on a fully-connected social graph, and finally utilizes different aggregators to collect different kinds of person-pair information. Unlike existing methods, the method—with its message passing mechanism in the graph model—can infer the social relationship of each person-pair in a joint way (i.e., not in isolation). Extensive experiments on People In Social Context (PISC)- and People In Photo Album (PIPA)-relation datasets show the superiority of our method compared to other methods.


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Understanding Social Relationships with Person-Pair Relations

Show Author's information Hang ZhaoHaicheng ChenLeilai LiHai Wan( )
Guizhou Post and Telecommunications Planning and Design Institute Co., Ltd., Guiyang 550003, China
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Ping An Technology (Shenzhen) Co., Ltd., Shenzhen 518049, China

Abstract

Social relationship understanding infers existing social relationships among individuals in a given scenario, which has been demonstrated to have a wide range of practical value in reality. However, existing methods infer the social relationship of each person pair in isolation, without considering the context-aware information for person pairs in the same scenario. The context-aware information for person pairs exists extensively in reality, that is, the social relationships of different person pairs in a simple scenario are always related to each other. For instance, if most of the person pairs in a simple scenario have the same social relationship, "friends", then the other pairs have a high probability of being "friends" or other similar coarse-level relationships, such as "intimate" . This context-aware information should thus be considered in social relationship understanding. Therefore, this paper proposes a novel end-to-end trainable Person-Pair Relation Network (PPRN), which is a GRU-based graph inference network, to first extract the visual and position information as the person-pair feature information, then enable it to transfer on a fully-connected social graph, and finally utilizes different aggregators to collect different kinds of person-pair information. Unlike existing methods, the method—with its message passing mechanism in the graph model—can infer the social relationship of each person-pair in a joint way (i.e., not in isolation). Extensive experiments on People In Social Context (PISC)- and People In Photo Album (PIPA)-relation datasets show the superiority of our method compared to other methods.

Keywords:

social relationship understanding, person-pair relations, Person-Pair Relation Network (PPRN)
Received: 20 October 2021 Accepted: 03 November 2021 Published: 25 January 2022 Issue date: June 2022
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Publication history

Received: 20 October 2021
Accepted: 03 November 2021
Published: 25 January 2022
Issue date: June 2022

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© The author(s) 2022.

Acknowledgements

This paper was supported by the National Natural Science Foundation of China (Nos. 61976232 and 51978675), Humanities and Social Science Research Project of Ministry of Education (No. 18YJCZH006), and All-China Federation of Returned Overseas Chinese Research Project (No. 17BZQK216).

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