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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1131-1137

Cross-modal recipe retrieval method based on modality semantic enhancement

Li Ming1
Zhou Dong2
Lei Fang1
Cao Buqing1
1. School of Computer Science & Engineering, Hunan University of Science & Technology, Xiangtan Hunan 411100, China
2. School of Information Science & Technology, Guangdong University of Foreign Studies, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0350

Abstract

Effectively representing features of modalities is a hot issue in cross-modal recipe retrieval. The current methods generally adopt two independent neural networks to extract the features of images and recipes respectively, achieving retrieval through cross-modal alignment. However, these methods mainly focus on the intra-modal information and ignore the intermodal interactions, resulting in the loss of some effective modality information. To address the problem, this paper proposed a cross-modal recipe retrieval method to enhance modality semantics through multimodal encoders. Firstly, it used a pre-trained model to extract initial semantic features of images and recipes and utilizing modality alignment to reduce the inter-model differences. Secondly, it employed the pairwise cross-modal attention to repeatedly reinforce the features of one modality by using features from another modality, extracted valid information. Thirdly, it used the self-attention mechanism to modal the internal features of modalities to capture rich modality-specific semantic information and potential associations. Finally, it introduced the triplet loss to minimize the distance between similar samples, achieved cross-modal retrieval learning. Experimental results on Recipe 1M dataset show that the proposed approach outperforms the current mainstream methods in terms of median ranking(MedR) and recall rate at top K(R@K), providing a powerful solution for cross-modal retrieval tasks.

Foundation Support

国家自然科学基金资助项目(62376062)
广东省哲学社会科学“十四五”规划项目(GD23CTS03)
广东省自然科学基金资助项目(2023A1515012718)
湖南省自然科学基金资助项目(2022JJ30020)
教育部人文社会科学研究资助项目(23YJAZH220)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0350
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: System Development & Application
Pages: 1131-1137
Serial Number: 1001-3695(2024)04-025-1131-07

Publish History

[2023-11-01] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

李明, 周栋, 雷芳, 等. 基于模态语义增强的跨模态食谱检索方法 [J]. 计算机应用研究, 2024, 41 (4): 1131-1137. (Li Ming, Zhou Dong, Lei Fang, et al. Cross-modal recipe retrieval method based on modality semantic enhancement [J]. Application Research of Computers, 2024, 41 (4): 1131-1137. )

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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