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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 11
3450-3455,3461

Fine-grained classification based on modal correlation learning

Zhang Tianshu
Liu Fan
Dai Wenwen
Gao Ruizhuo
School of Computer & Information, Hohai University, Nanjing 211100, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0137

Abstract

To address the problem of difficulty in distinguishing subtle differences between images in single-modal fine-grained classification methods, this paper introduced a multimodal fusion approach into the task of fine-grained classification. By fully utilizing the correlations and complementarity of multimodal data, this paper proposed a modality correlation learning-based fine-grained classification method. The method consisted of two stages. Firstly, considering the correspondence between image and text data, it used their matching degree as a constraint for model pretraining. Subsequently, with the loaded network parameters from the previous step, it firstly extracted multimodal features, and followed by utilizing text features to guide the generation of image features. Finally, it performed fine-grained classification based on the fused features. The method was trained and tested on the UPMC-Food101, MEP-3M-MEATS, and MEP-3M-OUTDOORS datasets, achieving accuracies of 91.13%, 82.39%, and 93.17%, respectively. Experimental results demonstrate that this method outperforms traditional multimodal fusion methods, making it an effective fine-grained classification approach.

Foundation Support

装备预研教育部联合基金资助项目
信息系统需求重点实验室开放基金资助项目(LHZZ2021-M04)
水下机器人技术重点实验室研究基金资助项目(2021JCJQ-SYSJJ-LB06905)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0137
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 11
Section: Technology of Graphic & Image
Pages: 3450-3455,3461
Serial Number: 1001-3695(2023)11-038-3450-06

Publish History

[2023-06-07] Accepted Paper
[2023-11-05] Printed Article

Cite This Article

张天舒, 刘凡, 戴雯雯, 等. 基于模态相关性学习的细粒度分类 [J]. 计算机应用研究, 2023, 40 (11): 3450-3455,3461. (Zhang Tianshu, Liu Fan, Dai Wenwen, et al. Fine-grained classification based on modal correlation learning [J]. Application Research of Computers, 2023, 40 (11): 3450-3455,3461. )

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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