University of Oulu

W. Chen et al., "Deep Learning for Instance Retrieval: A Survey," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 7270-7292, 1 June 2023, doi: 10.1109/TPAMI.2022.3218591

Deep learning for instance retrieval : a survey

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Author: Chen, Wei1; Liu, Yu2; Wang, Weiping1;
Organizations: 1Academy of Advanced Technology Research of Hunan, Changsha, China
2DUTRU International School of Information Science and Engineering, Dalian University of Technology, Dalian, China
3Leiden Institute of Advanced Computer Science, Leiden University, Leiden, EZ, The Netherlands
4Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
5Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 5.9 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-06-15


In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics. This abundance of content creation and sharing has introduced new challenges, particularly that of searching databases for similar content — Content Based Image Retrieval (CBIR) — a long-established research area in which improved efficiency and accuracy are needed for real-time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of instance search. In this survey we review recent instance retrieval works that are developed based on deep learning algorithms and techniques, with the survey organized by deep feature extraction, feature embedding and aggregation methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, whereby we identify milestone work, reveal connections among various methods and present the commonly used benchmarks, evaluation results, common challenges, and propose promising future directions.

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Series: IEEE transactions on pattern analysis and machine intelligence
ISSN: 0162-8828
ISSN-E: 2160-9292
ISSN-L: 0162-8828
Volume: 45
Issue: 6
Pages: 7270 - 7292
DOI: 10.1109/TPAMI.2022.3218591
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by China Scholarship Council under Grant 201703170183, in part by the Academy of Finland under Grant 331883, in part by Infotech Project FRAGES, and in part by the National Natural Science Foundation of China under Grants 61872379, 62022091, 61825305, and 62102061.
Academy of Finland Grant Number: 331883
Detailed Information: 331883 (Academy of Finland Funding decision)
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