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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1239-1246

Dual semantic correlation graph convolutional networks for cross-modal retrieval

Liu Jianan1
Fan Jingjing1
Zhao Jianguang1
Zhu Jie2
1. Information Engineering Institute, Hebei University of Architecture, Zhangjiakou Hebei 075000, China
2. College of Mathematics & Information Science, Hebei University, Baoding Hebei 071002, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0370

Abstract

With the continuous development of deep neural networks, significant progress has been made in the construction of cross-modal retrieval models. Cross-modal retrieval methods based on GCN have shown promising results in capturing semantic correlations in data, thus attracting increasing attention. However, most recent research focuses on incorporating correlations between labels and between samples into cross-modal representations, while the impact of correlations between label sets is neglected. In multi-label scenarios, the correlations between label sets can effectively describe semantic relationships between corresponding samples. Therefore, exploring the multi-label correlations and integrating it into cross-modal representations is important for improving the performance of cross-modal retrieval models. This paper proposed a dual semantic correlation graph convolutional networks(DSCGCN) cross-modal retrieval method. This method utilized GCN to explore the semantic correlations between labels and between multi-labels adaptively, and integrated the learned dual semantic correlations into the common representations. Additionally, it designed a multi-label similarity loss to make the similarities between the common representations more close to the semantic similarities. Experimental results on the NUS-WIDE, MIRFlickr-25K, and MS-COCO datasets demonstrate that because of multi-label correlations, DSCGCN achieves satisfactory retrieval performance.

Foundation Support

河北省自然科学基金资助项目(F2022511001)
河北省高等学校科学技术研究项目(ZC2022070)
河北大学高层次人才科研启动项目(521100223212)
张家口市市级科技计划财政资助项目(2311010A)
张家口市2022年度基础研究专项资助项目(2221008A)
河北建筑工程学院2024年校级研究生创新基金资助项目(XY2024068)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0370
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Technology of Graphic & Image
Pages: 1239-1246
Serial Number: 1001-3695(2024)04-041-1239-08

Publish History

[2023-11-02] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

刘佳楠, 范晶晶, 赵建光, 等. 基于二重语义相关性图卷积网络的跨模态检索方法 [J]. 计算机应用研究, 2024, 41 (4): 1239-1246. (Liu Jianan, Fan Jingjing, Zhao Jianguang, et al. Dual semantic correlation graph convolutional networks for cross-modal retrieval [J]. Application Research of Computers, 2024, 41 (4): 1239-1246. )

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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