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

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arxiv 2203.14713 v3 pith:5CYV2VEC submitted 2022-03-28 cs.IR cs.AI

Min Cao , Shiping Li , Juntao Li , Liqiang Nie , Min Zhang This is my paper · ORCID
classification cs.IRcs.AI
keywords approachesresearchretrievalcross-modalfeatureimage-textmodalityperspective
verification ladder T0 review T1 audit T2 compute T3 formal
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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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