- Title
- Few-shot image classification : current status and research trends
- Creator
- Liu, Ying; Zhang, Hengchang; Zhang, Weidong; Lu, Guojun; Tian, Qi; Ling, Nam
- Date
- 2022
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189267
- Identifier
- vital:17414
- Identifier
-
https://doi.org/10.3390/electronics11111752
- Identifier
- ISSN:2079-9292 (ISSN)
- Abstract
- Conventional image classification methods usually require a large number of training samples for the training model. However, in practical scenarios, the amount of available sample data is often insufficient, which easily leads to overfitting in network construction. Few-shot learning provides an effective solution to this problem and has been a hot research topic. This paper provides an intensive survey on the state-of-the-art techniques in image classification based on few-shot learning. According to the different deep learning mechanisms, the existing algorithms are di-vided into four categories: transfer learning based, meta-learning based, data augmentation based, and multimodal based methods. Transfer learning based methods transfer useful prior knowledge from the source domain to the target domain. Meta-learning based methods employ past prior knowledge to guide the learning of new tasks. Data augmentation based methods expand the amount of sample data with auxiliary information. Multimodal based methods use the information of the auxiliary modal to facilitate the implementation of image classification tasks. This paper also summarizes the few-shot image datasets available in the literature, and experimental results tested by some representative algorithms are provided to compare their performance and analyze their pros and cons. In addition, the application of existing research outcomes on few-shot image classification in different practical fields are discussed. Finally, a few future research directions are iden-tified. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Publisher
- MDPI
- Relation
- Electronics (Switzerland) Vol. 11, no. 11 (2022), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2022 by the author
- Rights
- Open Access
- Subject
- 4009 Electronics, sensors and digital hardware; Data augmentation; Few-shot learning; Meta-learning; Multimodal; Transfer learning
- Full Text
- Reviewed
- Funder
- This research was funded by National Natural Science Foundation of China, grant number 62106195, in part by the Graduate Innovation Fund Project of Xi’an University of Posts and Telecommunications, grant number CXJJDL2021013.
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