Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 11
3315-3320

Graph attention-based dual-branch method for social relationship recognition

Li Huan
Chen Niannian
College of Computer Science & Technology, Southwest University of Science & Technology, Mianyang Sichuan 621010, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0102

Abstract

Extracting social relationships between people from images has an important role in criminal investigation, privacy protection and other fields. Existing graph modeling approaches have achieved good results by creating interpersonal relationship graphs or constructing knowledge graphs to learn people's relationships. However, the methods based on graph convolutional neural network(GCN) ignore different degrees of importance of different features for specific relationships to some extent. In order to solve this problem, this paper proposed a graph attention-based double-branch social relationship recognition model(GAT-DBSR). The first branch extracting person regions as well as image global features as nodes, and the core updated these nodes to learn feature representations of person relationships through graph attention networks and gating mechanisms. The second branch extracted scene features by convolutional neural networks to enhance the recognition of relationships between people. Finally, it fused and classified the features of the two branches to obtain all social relationships. The model achieves an mAP of 74.4% on the fine-grained relationship recognition task on the PISC dataset, a 1.2% improvement compared to the baseline model. The accuracy of relationship recognition on the PIPA dataset also shows some improvement. The experimental results show that the model has better results.

Foundation Support

四川省科技厅重点研发项目(2021YFG0031)
四川省省级科研院所科技成果转化项目(22YSZH0021)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0102
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 11
Section: Algorithm Research & Explore
Pages: 3315-3320
Serial Number: 1001-3695(2023)11-017-3315-06

Publish History

[2023-06-06] Accepted Paper
[2023-11-05] Printed Article

Cite This Article

李欢, 陈念年. 一种基于图注意力的双分支社会关系识别方法 [J]. 计算机应用研究, 2023, 40 (11): 3315-3320. (Li Huan, Chen Niannian. Graph attention-based dual-branch method for social relationship recognition [J]. Application Research of Computers, 2023, 40 (11): 3315-3320. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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