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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 8
2367-2374

Research on multi-modal model based on cross-modal multi-dimensional relationship enhancement

Cheng Xi1a,1b
Yang Guan1a,1b
Liu Xiaoming1a,1b
Liu Yang2
1. a. School of Computer Science, b. Henan Key Laboratory on Public Opinion Intelligent Analysis, Zhongyuan University of Technology, Zhengzhou 450007, China
2. School of Telecommunications Engineering, Xidian University, Xi'an 710071, China
DOI: 10.19734/j.issn.1001-3695.2022.11.0517

Abstract

Aiming at the problem that the current multi-modal models can't fully excavate the spatial relationship of non-significant regions and the semantic relationship between contexts, resulting in poor inference of multimodal relationship, this paper proposed a multi-modal model based on cross-modal multi-dimensional relationship enhancement, which was used to extract the spatial relation information between the image elements under the latent layer structure, and reasoning the semantic correlation between visual and language. Firstly, the model designed a feature diversity module to mine the sub-significant region features associated with significant regions in the image, thus enhancing the image spatial relationship feature representation. Secondly, it learned the context relationship of language in the image by the context guided attention module to achieve cross modal relationship alignment. Experiments on the MSCOCO dataset show that the proposed model achieves better performance, with BLEU-4 and CIDEr scores are improved by 0.5% and 1.3%, respectively. This approach is also applied to the visual question answering task, and the performance is improved by 0.62% on the VQA 2.0 dataset, which proves the wide applicability of the approach in multimodal tasks.

Foundation Support

国家自然科学基金青年资助项目(61906141)
河南省高等学校重点科研资助项目(23A520022)
东北师范大学应用统计教育部重点实验室资助项目(135131007)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.11.0517
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 8
Section: Algorithm Research & Explore
Pages: 2367-2374
Serial Number: 1001-3695(2023)08-019-2367-08

Publish History

[2023-01-05] Accepted Paper
[2023-08-05] Printed Article

Cite This Article

成曦, 杨关, 刘小明, 等. 基于跨模态多维关系增强的多模态模型研究 [J]. 计算机应用研究, 2023, 40 (8): 2367-2374. (Cheng Xi, Yang Guan, Liu Xiaoming, et al. Research on multi-modal model based on cross-modal multi-dimensional relationship enhancement [J]. Application Research of Computers, 2023, 40 (8): 2367-2374. )

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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