Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (3): 1-12.doi: 10.16088/j.issn.1001-6600.2021071302

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Progress of Cross-modal Retrieval Methods Based on Representation Learning

DU Jinfeng, WANG Hairong*, LIANG Huan, WANG Dong   

  1. Department of Computer Science and Engineering, North Minzu University, Yinchuan Ningxia 750021, China
  • Received:2021-07-13 Revised:2021-10-09 Online:2022-05-25 Published:2022-05-27

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Abstract: With the rapid growth of multi-modal data, the application requirements of cross-modal retrieval are brought, and the research on cross-modal retrieval methods is proposed. This paper traces the latest progress in this field, tracks and deeply studies the cross-modal retrieval methods based on representation learning at home and abroad, defines the cross-modal retrieval problems, and combs the common technical methods, mainstream models, common data sets, evaluation methods and main challenges in this field. This paper mainly introduces the cross-modal retrieval method based on representation learning from three aspects:statistical correlation analysis, graph regularization and metric learning, and analyzes its advantages and disadvantages. In order to analyze the advantages and disadvantages of the above methods, 14 methods are reproduced on four data sets for comparative evaluation. The experimental results show that the training method based on statistical correlation analysis is efficient and easy to implement; Based on graph regularization method, semantic association is realized by mining the similarity between and within modes; The metric-based learning method is to preserve the semantically similar / dissimilar information of data in the common subspace as much as possible. To sum up, this paper introduces the research status of cross-modal retrieval methods based on representation learning, which provided a reference for the research of cross-modal retrieval methods.

Key words: multi-modal data, cross-modal retrieval, statistical correlation analysis, graph regularization, metric learning

CLC Number: 

  • TP391.3
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