Image-text Retrieval: A Survey on Recent Research and Development

• URL: http://arxiv.org/abs/2203.14713v1
• Date: Mon, 28 Mar 2022 13:00:01 GMT
• Title: Image-text Retrieval: A Survey on Recent Research and Development
• Authors: Min Cao, Shiping Li, Juntao Li, Liqiang Nie, Min Zhang
• Abstract summary: Cross-modal image-text retrieval (ITR) has experienced increased interest in the research community due to its excellent research value and broad real-world application. This paper presents a comprehensive and up-to-date survey on the ITR approaches from four perspectives.
• Score: 58.060687870247996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the past few years, cross-modal image-text retrieval (ITR) has experienced increased interest in the research community due to its excellent research value and broad real-world application. It is designed for the scenarios where the queries are from one modality and the retrieval galleries from another modality. This paper presents a comprehensive and up-to-date survey on the ITR approaches from four perspectives. By dissecting an ITR system into two processes: feature extraction and feature alignment, we summarize the recent advance of the ITR approaches from these two perspectives. On top of this, the efficiency-focused study on the ITR system is introduced as the third perspective. To keep pace with the times, we also provide a pioneering overview of the cross-modal pre-training ITR approaches as the fourth perspective. Finally, we outline the common benchmark datasets and valuation metric for ITR, and conduct the accuracy comparison among the representative ITR approaches. Some critical yet less studied issues are discussed at the end of the paper.

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