Deep learning for instance retrieval : a survey
Chen, Wei; Liu, Yu; Wang, Weiping; Bakker, Erwin M.; Georgiou, Theodoros; Fieguth, Paul; Liu, Li (2022-11-01)
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
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2023061555340
Tiivistelmä
Abstract
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.
Kokoelmat
- Avoin saatavuus [37957]