{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:21:26Z","timestamp":1778271686032,"version":"3.51.4"},"reference-count":142,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The tasks of few-shot, one-shot, and zero-shot learning\u2014or collectively \u201clow-shot learning\u201d (LSL)\u2014at first glance are quite similar to the long-standing task of class imbalanced learning; specifically, they aim to learn classes for which there is little labeled data available. Motivated by this similarity, we conduct a survey to review the recent literature for works which combine these fields in one of two ways, either addressing the obstacle of class imbalance within a LSL setting, or utilizing LSL techniques or frameworks in order to combat class imbalance within other settings. In our survey of over 60 papers in a wide range of applications from January 2020 to July 2023 (inclusive), we examine and report methodologies and experimental results, find that most works report performance at or above their respective state-of-the-art, and highlight current research gaps which hold potential for future work, especially those involving the use of LSL techniques in imbalanced tasks. To this end, we emphasize the lack of works utilizing LSL approaches based on large language models or semantic data, and works using LSL for big-data imbalanced tasks.<\/jats:p>","DOI":"10.1186\/s40537-023-00851-z","type":"journal-article","created":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T17:02:40Z","timestamp":1704214960000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Low-shot learning and class imbalance: a survey"],"prefix":"10.1186","volume":"11","author":[{"given":"Preston","family":"Billion Polak","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph D.","family":"Prusa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taghi M.","family":"Khoshgoftaar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,2]]},"reference":[{"issue":"1","key":"851_CR1","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s40537-018-0151-6","volume":"5","author":"JL Leevy","year":"2018","unstructured":"Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N. A survey on addressing high-class imbalance in big data. J Big Data. 2018;5(1):42. https:\/\/doi.org\/10.1186\/s40537-018-0151-6.","journal-title":"J Big Data"},{"issue":"1","key":"851_CR2","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson JM, Khoshgoftaar TM. Survey on deep learning with class imbalance. J Big Data. 2019;6(1):27. https:\/\/doi.org\/10.1186\/s40537-019-0192-5.","journal-title":"J Big Data"},{"key":"851_CR3","unstructured":"Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning. In: Advances in neural information processing systems, vol. 30. Curran Associates, Inc.; 2017. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/hash\/cb8da6767461f2812ae4290eac7cbc42-Abstract.html. Accessed 30 June 2023."},{"key":"851_CR4","unstructured":"Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th international conference on machine learning. PMLR; 2017. ISSN: 2640-3498. p. 1126\u201335. https:\/\/proceedings.mlr.press\/v70\/finn17a.html. Accessed 11 June 2023."},{"key":"851_CR5","doi-asserted-by":"crossref","unstructured":"Xian Y, Schiele B, Akata Z. Zero-shot learning\u2014the good, the bad and the ugly; 2017. p. 4582\u201391. https:\/\/openaccess.thecvf.com\/content_cvpr_2017\/html\/Xian_Zero-Shot_Learning_-_CVPR_2017_paper.html. Accessed 23 Oct 2023.","DOI":"10.1109\/CVPR.2017.328"},{"key":"851_CR6","doi-asserted-by":"publisher","unstructured":"Chen Z, Huang Y, Chen J, Geng Y, Zhang W, Fang Y, Pan JZ, Chen H. DUET: cross-modal semantic grounding for contrastive zero-shot learning. In: Proceedings of the AAAI conference on artificial intelligence. 2023;37(1):405\u201313. https:\/\/doi.org\/10.1609\/aaai.v37i1.25114. Number: 1. Accessed 24 Aug 2023.","DOI":"10.1609\/aaai.v37i1.25114"},{"key":"851_CR7","unstructured":"Sussman GJ, Yip K. Sparse representations for fast, one-shot learning. AI Memos; 1997. https:\/\/dspace.mit.edu\/handle\/1721.1\/6673. Accessed 23 Oct 2023."},{"key":"851_CR8","unstructured":"Triantafillou E, Zemel R, Urtasun R. Few-shot learning through an information retrieval lens. In: Advances in neural information processing systems, vol. 30. Curran Associates, Inc.; 2017. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/01e9565cecc4e989123f9620c1d09c09-Abstract.html. Accessed 23 Oct 2023."},{"key":"851_CR9","unstructured":"Parnami A, Lee M. Learning from few examples: a summary of approaches to few-shot learning; 2022. https:\/\/arxiv.org\/abs\/2203.04291v1. Accessed 30 June 2023."},{"key":"851_CR10","doi-asserted-by":"publisher","unstructured":"Bromley J, Guyon I, LeCun Y, S\u00e4ckinger E, Shah R. Signature Verification using a \u201cSiamese\u201d time delay neural network. In: Advances in neural information processing systems, vol. 6. Morgan-Kaufmann; 1993. https:\/\/doi.org\/10.1142\/S0218001493000339. https:\/\/proceedings.neurips.cc\/paper\/1993\/hash\/288cc0ff022877bd3df94bc9360b9c5d-Abstract.html. Accessed 29 June 2023.","DOI":"10.1142\/S0218001493000339"},{"key":"851_CR11","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler D, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020;33:1877\u2013901.","journal-title":"Adv Neural Inf Process Syst"},{"key":"851_CR12","unstructured":"Tax DMJ. One-class classification: concept learning in the absence of counter-examples. PhD Thesis, Delft University of Technology (TU Delft); 2002. https:\/\/www.elibrary.ru\/item.asp?id=5230402. Accessed 29 June 2023."},{"key":"851_CR13","doi-asserted-by":"crossref","unstructured":"Zhai X, Wang X, Mustafa B, Steiner A, Keysers D, Kolesnikov A, Beyer L. LiT: zero-shot transfer with locked-image text tuning; 2022. p. 18123\u201333. https:\/\/openaccess.thecvf.com\/content\/CVPR2022\/html\/Zhai_LiT_Zero-Shot_Transfer_With_Locked-Image_Text_Tuning_CVPR_2022_paper.html. Accessed 30 June 2023.","DOI":"10.1109\/CVPR52688.2022.01759"},{"key":"851_CR14","doi-asserted-by":"publisher","unstructured":"Van\u00a0Hulse J, Khoshgoftaar TM, Napolitano A. Experimental perspectives on learning from imbalanced data. In: Proceedings of the 24th international conference on machine learning. ICML \u201907. Association for computing machinery, New York, NY, USA; 2007. p. 935\u201342. https:\/\/doi.org\/10.1145\/1273496.1273614. Accessed 23 Oct 2023.","DOI":"10.1145\/1273496.1273614"},{"issue":"9","key":"851_CR15","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21(9):1263\u201384. https:\/\/doi.org\/10.1109\/TKDE.2008.239.","journal-title":"IEEE Trans Knowl Data Eng."},{"key":"851_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3298303","author":"M Ochal","year":"2023","unstructured":"Ochal M, Patacchiola M, Vazquez J, Storkey A, Wang S. Few-shot learning with class imbalance. IEEE Trans Artif Intell. 2023. https:\/\/doi.org\/10.1109\/TAI.2023.3298303.","journal-title":"IEEE Trans Artif Intell"},{"key":"851_CR17","doi-asserted-by":"publisher","unstructured":"Zhou J, Li B, Wang P, Li P, Gan W, Wu W, Yan J, Ouyang W. Real-time visual object tracking via few-shot learning; 2021. arXiv preprint arXiv:2103.10130. https:\/\/doi.org\/10.48550\/arxiv.2103.10130","DOI":"10.48550\/arxiv.2103.10130"},{"key":"851_CR18","doi-asserted-by":"publisher","unstructured":"Zhang Z, Zhoa J, Liang X. Zero-shot learning based on semantic embedding for ship detection. In: 2020 3rd international conference on unmanned systems (ICUS). IEEE; 2020. p. 1152\u20136. https:\/\/doi.org\/10.1109\/ICUS50048.2020.9274981","DOI":"10.1109\/ICUS50048.2020.9274981"},{"key":"851_CR19","doi-asserted-by":"crossref","unstructured":"Rahman S, Khan S, Barnes N. Improved visual-semantic alignment for zero-shot object detection. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, p. 11932\u20139; 2020. Issue: 07","DOI":"10.1609\/aaai.v34i07.6868"},{"issue":"7","key":"851_CR20","doi-asserted-by":"publisher","first-page":"783","DOI":"10.3390\/electronics10070783","volume":"10","author":"Y Gao","year":"2021","unstructured":"Gao Y, Hou R, Gao Q, Hou Y. A fast and accurate few-shot detector for objects with fewer pixels in drone image. Electronics. 2021;10(7):783. https:\/\/doi.org\/10.3390\/electronics10070783.","journal-title":"Electronics"},{"key":"851_CR21","unstructured":"Wolters P, Daw C, Hutchinson B, Phillips L. Proposal-based few-shot sound event detection for speech and environmental sounds with perceivers. arXiv preprint; 2021. arXiv:2107.13616"},{"key":"851_CR22","doi-asserted-by":"publisher","unstructured":"Huang K, Geng J, Jiang W, Deng X, Xu Z. Pseudo-loss confidence metric for semi-supervised few-shot learning. In: Proceedings of the IEEE\/CVF international conference on computer vision; 2021. p. 8671\u201380. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00855","DOI":"10.1109\/ICCV48922.2021.00855"},{"key":"851_CR23","doi-asserted-by":"crossref","unstructured":"Yang H, Cai S, Sheng H, Deng B, Huang J, Hua X-S, Tang Y, Zhang Y. Balanced and hierarchical relation learning for one-shot object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2022. p. 7591\u2013600.","DOI":"10.1109\/CVPR52688.2022.00744"},{"key":"851_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2021.103094","volume":"116","author":"T Liu","year":"2021","unstructured":"Liu T, Yang Y, Fan W, Wu C. Few-shot learning for cardiac arrhythmia detection based on electrocardiogram data from wearable devices. Dig Signal Process. 2021;116: 103094. https:\/\/doi.org\/10.1016\/j.dsp.2021.103094.","journal-title":"Dig Signal Process"},{"key":"851_CR25","unstructured":"Olah J, Baruah S, Bose D, Narayanan S. Cross domain emotion recognition using few shot knowledge transfer; 2021. arXiv preprint arXiv:2110.05021"},{"key":"851_CR26","doi-asserted-by":"publisher","unstructured":"Zhang L, Zhou S, Guan J, Zhang J. Accurate few-shot object detection with support-query mutual guidance and hybrid loss. In: 2021 IEEE\/cvf conference on computer vision and pattern recognition (CVPR); 2021. p. 14419\u201327. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01419. ISSN: 2575-7075","DOI":"10.1109\/CVPR46437.2021.01419"},{"key":"851_CR27","doi-asserted-by":"publisher","unstructured":"Ma S, Li X, Tang J, Guo F. A zero-shot method for 3d medical image segmentation. In: 2021 IEEE international conference on multimedia and expo (ICME). IEEE; 2021. p. 1\u20136. https:\/\/doi.org\/10.1109\/icme51207.2021.9428261","DOI":"10.1109\/icme51207.2021.9428261"},{"key":"851_CR28","doi-asserted-by":"publisher","unstructured":"Masihullah S, Garg R, Mukherjee P, Ray A. Attention based coupled framework for road and pothole segmentation. In: 2020 25th international conference on pattern recognition (ICPR). IEEE; 2021. p. 5812\u20139. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412368","DOI":"10.1109\/ICPR48806.2021.9412368"},{"issue":"5","key":"851_CR29","doi-asserted-by":"publisher","first-page":"3248","DOI":"10.1109\/TII.2021.3107785","volume":"18","author":"Y Hua","year":"2021","unstructured":"Hua Y, Yi D. Synthetic to realistic imbalanced domain adaption for urban scene perception. IEEE Trans Ind Inform. 2021;18(5):3248\u201355. https:\/\/doi.org\/10.1109\/TII.2021.3107785.","journal-title":"IEEE Trans Ind Inform"},{"key":"851_CR30","doi-asserted-by":"crossref","unstructured":"Yuan Y, Chen W, Chen T, Yang Y, Ren Z, Wang Z, Hua G. Calibrated domain-invariant learning for highly generalizable large scale re-identification. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision; 2020. p. 3589\u201398.","DOI":"10.1109\/WACV45572.2020.9093521"},{"key":"851_CR31","doi-asserted-by":"publisher","first-page":"2778","DOI":"10.1109\/JSTARS.2020.2995703","volume":"13","author":"L Zhang","year":"2020","unstructured":"Zhang L, Zhang C, Quan S, Xiao H, Kuang G, Liu L. A class imbalance loss for imbalanced object recognition. IEEE J Select Top Appl Earth Obs Remote Sens. 2020;13:2778\u201392. https:\/\/doi.org\/10.1109\/JSTARS.2020.2995703.","journal-title":"IEEE J Select Top Appl Earth Obs Remote Sens"},{"key":"851_CR32","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neucom.2021.03.073","volume":"449","author":"Q Li","year":"2021","unstructured":"Li Q, Zhang Y, Sun S, Zhao X, Li K, Tan M. Rethinking semantic-visual alignment in zero-shot object detection via a softplus margin focal loss. Neurocomputing. 2021;449:117\u201335. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.073.","journal-title":"Neurocomputing"},{"key":"851_CR33","doi-asserted-by":"publisher","unstructured":"Olson AW, Cucu A, Bock T. Multi-class zero-shot learning for artistic material recognition; 2020. arXiv preprint arXiv:2010.13850https:\/\/doi.org\/10.48550\/arxiv.2010.13850","DOI":"10.48550\/arxiv.2010.13850"},{"key":"851_CR34","doi-asserted-by":"crossref","unstructured":"Wu C, Wang B, Liu S, Liu X, Wu P. TD-sampler: learning a training difficulty based sampling strategy for few-shot object detection. In: 2022 7th international conference on cloud computing and big data analytics (ICCCBDA). IEEE; 2022. p. 275\u20139.","DOI":"10.1109\/ICCCBDA55098.2022.9778859"},{"key":"851_CR35","doi-asserted-by":"publisher","unstructured":"Chen R, Chen T, Hui X, Wu H, Li G, Lin L. Knowledge graph transfer network for few-shot recognition. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34; 2020. p. 10575\u201382. https:\/\/doi.org\/10.48550\/arxiv.1911.09579. Issue: 07.","DOI":"10.48550\/arxiv.1911.09579"},{"key":"851_CR36","doi-asserted-by":"publisher","unstructured":"Malik V, Kumar A, Veppa J. Exploring the limits of natural language inference based setup for few-shot intent detection; 2021. arXiv preprint arXiv:2112.07434. https:\/\/doi.org\/10.48550\/arxiv.2112.07434","DOI":"10.48550\/arxiv.2112.07434"},{"key":"851_CR37","unstructured":"Li G, Zhai Y, Chen Q, Gao X, Zhang J, Zhang Y. Continual few-shot intent detection. In: Proceedings of the 29th international conference on computational linguistics; 2022. p. 333\u201343."},{"key":"851_CR38","first-page":"9290","volume":"34","author":"O Veilleux","year":"2021","unstructured":"Veilleux O, Boudiaf M, Piantanida P, Ben Ayed I. Realistic evaluation of transductive few-shot learning. Adv Neural Inf Process Syst. 2021;34:9290\u2013302.","journal-title":"Adv Neural Inf Process Syst"},{"key":"851_CR39","unstructured":"Hess S, Ditzler G. A maximum log-likelihood method for imbalanced few-shot learning tasks; 2022. https:\/\/arxiv.org\/abs\/2211.14668."},{"key":"851_CR40","unstructured":"Tao R, Chen H, Savvides M. Towards class-balanced transductive few-shot learning; 2022. https:\/\/openreview.net\/forum?id=ZA_F5AU-byh."},{"key":"851_CR41","unstructured":"Wertheimer D, Tang L, Baijal D, Mittal P, Talwar A, Hariharan B. Few-shot learning in long-tailed settings. https:\/\/daviswer.github.io\/files\/tpami2021paper.pdf."},{"key":"851_CR42","doi-asserted-by":"crossref","unstructured":"Wang Y, Yu Z, Wang J, Heng Q, Chen H, Ye W, Xie R, Xie X, Zhang S. Exploring vision-language models for imbalanced learning; 2023. https:\/\/link.springer.com\/article\/10.1007\/s11263-023-01868-w.","DOI":"10.1007\/s11263-023-01868-w"},{"issue":"12","key":"851_CR43","doi-asserted-by":"publisher","first-page":"25249","DOI":"10.1109\/TITS.2022.3199805","volume":"23","author":"L Deng","year":"2022","unstructured":"Deng L, He C, Xu G, Zhu H, Wang H. PcGAN: a noise robust conditional generative adversarial network for one shot learning. IEEE Trans Intell Transp Syst. 2022;23(12):25249\u201358.","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"851_CR44","doi-asserted-by":"crossref","unstructured":"He C, Wang R, Chen X. A tale of two CILs: the connections between class incremental learning and class imbalanced learning, and beyond. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2021. p. 3559\u201369","DOI":"10.1109\/CVPRW53098.2021.00395"},{"key":"851_CR45","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2022.880729","volume":"5","author":"LN Smith","year":"2022","unstructured":"Smith LN, Conovaloff A. Building one-shot semi-supervised (BOSS) learning up to fully supervised performance. Front Artif Intell. 2022;5: 880729. https:\/\/doi.org\/10.3389\/frai.2022.880729.","journal-title":"Front Artif Intell"},{"key":"851_CR46","unstructured":"Arfeen A, Dutta T, Biswas S. Handling class-imbalance for improved zero-shot domain generalization. BMVC; 2022."},{"issue":"7","key":"851_CR47","doi-asserted-by":"publisher","first-page":"6543","DOI":"10.1109\/TCYB.2020.3004641","volume":"52","author":"Z Ji","year":"2021","unstructured":"Ji Z, Yu X, Yu Y, Pang Y, Zhang Z. Semantic-guided class-imbalance learning model for zero-shot image classification. IEEE Trans Cybernetics. 2021;52(7):6543\u201354.","journal-title":"IEEE Trans Cybernetics"},{"key":"851_CR48","doi-asserted-by":"crossref","unstructured":"Ye C, Barnes N, Petersson L, Tsuchida R. Efficient Gaussian process model on class-imbalanced datasets for generalized zero-shot learning. In: 2022 26th international conference on pattern recognition (ICPR). IEEE; 2022. p. 2078\u201385.","DOI":"10.1109\/ICPR56361.2022.9956666"},{"key":"851_CR49","unstructured":"Majee A, Agrawal K, Subramanian A. Few-shot learning for road object detection. In: AAAI workshop on meta-learning and MetaDL challenge. PMLR; 2021. p. 115\u201326."},{"key":"851_CR50","doi-asserted-by":"crossref","unstructured":"Agarwal A, Majee A, Subramanian A, Arora C. Attention guided cosine margin for overcoming class-imbalance in few-shot road object detection; 2022. https:\/\/ieeexplore.ieee.org\/document\/9707579","DOI":"10.1109\/WACVW54805.2022.00028"},{"issue":"19","key":"851_CR51","doi-asserted-by":"publisher","first-page":"3816","DOI":"10.3390\/rs13193816","volume":"13","author":"X Huang","year":"2021","unstructured":"Huang X, He B, Tong M, Wang D, He C. Few-shot object detection on remote sensing images via shared attention module and balanced fine-tuning strategy. Remote Sens. 2021;13(19):3816. https:\/\/doi.org\/10.3390\/rs13193816.","journal-title":"Remote Sens"},{"issue":"14","key":"851_CR52","doi-asserted-by":"publisher","first-page":"3255","DOI":"10.3390\/rs14143255","volume":"14","author":"Y Wang","year":"2022","unstructured":"Wang Y, Xu C, Liu C, Li Z. Context information refinement for few-shot object detection in remote sensing images. Remote Sens. 2022;14(14):3255.","journal-title":"Remote Sens"},{"key":"851_CR53","doi-asserted-by":"crossref","unstructured":"Ouyang C, Biffi C, Chen C, Kart T, Qiu H, Rueckert D. Self-supervision with superpixels: training few-shot medical image segmentation without annotation. In: Computer vision-ECCV 2020: 16th European conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIX 16. Springer; 2020. p. 762\u201380.","DOI":"10.1007\/978-3-030-58526-6_45"},{"key":"851_CR54","doi-asserted-by":"crossref","unstructured":"Dutta T, Singh A, Biswas S. Adaptive margin diversity regularizer for handling data imbalance in zero-shot sbir. In: Proceedings, Part V 16, computer vision-ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328. Springer; 2020. p. 349\u201364.","DOI":"10.1007\/978-3-030-58558-7_21"},{"key":"851_CR55","doi-asserted-by":"publisher","unstructured":"Wu G, Gong S. Generalising without forgetting for lifelong person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35; 2021. p. 2889\u201397 . https:\/\/doi.org\/10.1609\/aaai.v35i4.16395","DOI":"10.1609\/aaai.v35i4.16395"},{"issue":"12","key":"851_CR56","doi-asserted-by":"publisher","first-page":"2855","DOI":"10.1049\/iet-ipr.2019.0618","volume":"14","author":"Z Chen","year":"2020","unstructured":"Chen Z, Ma W, Xu N, Ji C, Zhang Y. SiameseCCR: a novel method for one-shot and few-shot Chinese CAPTCHA recognition using deep Siamese network. IET Image Process. 2020;14(12):2855\u20139. https:\/\/doi.org\/10.1049\/iet-ipr.2019.0618.","journal-title":"IET Image Process"},{"issue":"15","key":"851_CR57","doi-asserted-by":"publisher","first-page":"10751","DOI":"10.1007\/s00521-023-08262-0","volume":"35","author":"Y Wang","year":"2023","unstructured":"Wang Y, Wei Y, Zhang Y, Jin C, Xin G, Wang B. Few-shot learning in realistic settings for text CAPTCHA recognition. Neural Comput Appl. 2023;35(15):10751\u201364. https:\/\/doi.org\/10.1007\/s00521-023-08262-0.","journal-title":"Neural Comput Appl"},{"key":"851_CR58","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3294975","author":"W Lei","year":"2023","unstructured":"Lei W, Su Q, Jiang T, Gu R, Wang N, Liu X, Wang G, Zhang X, Zhang S. One-shot weakly-supervised segmentation in 3D medical images. IEEE Trans Med Imaging. 2023. https:\/\/doi.org\/10.1109\/TMI.2023.3294975.","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"851_CR59","doi-asserted-by":"publisher","first-page":"2656","DOI":"10.1109\/TMI.2020.3045775","volume":"40","author":"H Cui","year":"2020","unstructured":"Cui H, Wei D, Ma K, Gu S, Zheng Y. A unified framework for generalized low-shot medical image segmentation with scarce data. IEEE Trans Med Imaging. 2020;40(10):2656\u201371. https:\/\/doi.org\/10.1109\/TMI.2020.3045775.","journal-title":"IEEE Trans Med Imaging"},{"key":"851_CR60","first-page":"15787","volume":"34","author":"J Bragg","year":"2021","unstructured":"Bragg J, Cohan A, Lo K, Beltagy I. Flex: unifying evaluation for few-shot nlp. Adv Neural Inf Process Syst. 2021;34:15787\u2013800.","journal-title":"Adv Neural Inf Process Syst"},{"key":"851_CR61","doi-asserted-by":"crossref","unstructured":"Kim M, Yang Y, Ryu JH, Kim T. Meta-learning with adaptive weighted loss for imbalanced cold-start recommendation; 2023. https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614965.","DOI":"10.1145\/3583780.3614965"},{"key":"851_CR62","volume-title":"Center loss guided prototypical networks for unbalance few-shot industrial fault diagnosis. Mobile information systems","author":"T Yu","year":"2022","unstructured":"Yu T, Guo H, Zhu Y. Center loss guided prototypical networks for unbalance few-shot industrial fault diagnosis. Mobile information systems. Hindawi; 2022."},{"key":"851_CR63","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3232394","author":"K Li","year":"2022","unstructured":"Li K, Shang C, Ye H. Reweighted regularized prototypical network for few-shot fault diagnosis. IEEE Trans Neural Netw Learn Syst. 2022. https:\/\/doi.org\/10.1109\/TNNLS.2022.3232394.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"851_CR64","unstructured":"Vinyals O, Blundell C, Lillicrap T, kavukcuoglu k, Wierstra D. Matching networks for one shot learning. In: Advances in neural information processing systems, vol. 29. Curran Associates, Inc.; 2016. https:\/\/proceedings.neurips.cc\/paper\/2016\/hash\/90e1357833654983612fb05e3ec9148c-Abstract.html. Accessed 11 June 2023."},{"key":"851_CR65","unstructured":"Wah C, Branson S, Welinder P, Perona P, Belongie S. The Caltech-UCSD Birds-200-2011 Dataset. https:\/\/www.vision.caltech.edu\/datasets\/cub_200_2011\/."},{"key":"851_CR66","unstructured":"Chen W-Y, Liu Y-C, Kira Z, Wang Y-CF, Huang J-B. A closer look at few-shot classification; 2018. https:\/\/openreview.net\/forum?id=HkxLXnAcFQ. Accessed 03June 2023."},{"key":"851_CR67","unstructured":"Hu Y, Gripon V, Pateux S. Leveraging the feature distribution in transfer-based few-shot learning; 2021. arXiv. arXiv:2006.03806 [cs, stat]. http:\/\/arxiv.org\/abs\/2006.03806. Accessed 10 May 2023."},{"key":"851_CR68","unstructured":"Boudiaf M, Masud ZI, Rony J, Dolz J, Piantanida P, Ayed IB. Transductive information maximization for few-shot learning; 2020. arXiv. arXiv:2008.11297 [cs, stat]. http:\/\/arxiv.org\/abs\/2008.11297. Accessed 10 May 2023."},{"key":"851_CR69","unstructured":"Ren M, Triantafillou E, Ravi S, Snell J, Swersky K, Tenenbaum JB, Larochelle H, Zemel RS. Meta-learning for semi-supervised few-shot classification; 2018. https:\/\/openreview.net\/forum?id=HJcSzz-CZ. Accessed 18 July 2023."},{"key":"851_CR70","unstructured":"Ziko I, Dolz J, Granger E, Ayed IB. Laplacian regularized few-shot learning. In: International conference on machine learning. PMLR; 2020. p. 11660\u201370."},{"key":"851_CR71","unstructured":"Triantafillou E, Zhu T, Dumoulin V, Lamblin P, Evci U, Xu K, Goroshin R, Gelada C, Swersky K, Manzagol P-A, Larochelle H. Meta-dataset: a dataset of datasets for learning to learn from few examples; 2019. https:\/\/openreview.net\/forum?id=rkgAGAVKPr. Accessed 21 July 2023."},{"key":"851_CR72","doi-asserted-by":"publisher","unstructured":"Wertheimer D, Hariharan B. Few-shot learning with localization in realistic settings. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR); 2019. p. 6551\u201360. https:\/\/doi.org\/10.1109\/CVPR.2019.00672. ISSN: 2575-7075.","DOI":"10.1109\/CVPR.2019.00672"},{"key":"851_CR73","doi-asserted-by":"crossref","unstructured":"Van\u00a0Horn G, Mac\u00a0Aodha O, Song Y, Cui Y, Sun C, Shepard A, Adam H, Perona P, Belongie S. The INaturalist species classification and detection dataset. 2018. p. 8769\u201378. https:\/\/openaccess.thecvf.com\/content_cvpr_2018\/html\/Van_Horn_The_INaturalist_Species_CVPR_2018_paper.html. Accessed 18 July 2023.","DOI":"10.1109\/CVPR.2018.00914"},{"key":"851_CR74","doi-asserted-by":"publisher","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, Sutskever I. Learning transferable visual models from natural language supervision; 2021. arXiv. arXiv:2103.00020 [cs]. https:\/\/doi.org\/10.48550\/arXiv.2103.00020. http:\/\/arxiv.org\/abs\/2103.00020. Accessed 04 Sept 2023.","DOI":"10.48550\/arXiv.2103.00020"},{"key":"851_CR75","doi-asserted-by":"crossref","unstructured":"Zhang S, Li Z, Yan S, He X, Sun J. Distribution alignment: a unified framework for long-tail visual recognition; 2021. p. 2361\u201370. https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Zhang_Distribution_Alignment_A_Unified_Framework_for_Long-Tail_Visual_Recognition_CVPR_2021_paper.html. Accessed 04 Sept 2023.","DOI":"10.1109\/CVPR46437.2021.00239"},{"key":"851_CR76","doi-asserted-by":"crossref","unstructured":"Liu Z, Miao Z, Zhan X, Wang J, Gong B, Yu SX. Large-scale long-tailed recognition in an open world; 2019. p. 2537\u201346. https:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Liu_Large-Scale_Long-Tailed_Recognition_in_an_Open_World_CVPR_2019_paper.html. Accessed 25 July 2023.","DOI":"10.1109\/CVPR.2019.00264"},{"key":"851_CR77","unstructured":"Krizhevsky A. Learning multiple layers of features from tiny images; 2009. https:\/\/www.semanticscholar.org\/paper\/Learning-Multiple-Layers-of-Features-from-Tiny-Krizhevsky\/5d90f06bb70a0a3dced62413346235c02b1aa086. Accessed 21 July 2023."},{"key":"851_CR78","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition; 2009. p. 248\u201355. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848. ISSN: 1063-6919.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"851_CR79","unstructured":"Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY. Reading digits in natural images with unsupervised feature learning. In: NIPS workshop on deep learning and unsupervised feature learning 2011; 2011. http:\/\/ufldl.stanford.edu\/housenumbers\/nips2011_housenumbers.pdf. Accessed 21 July 2023."},{"key":"851_CR80","doi-asserted-by":"crossref","unstructured":"Peng X, Bai Q, Xia X, Huang Z, Saenko K, Wang B. Moment matching for multi-source domain adaptation; 2019. p. 1406\u201315. https:\/\/openaccess.thecvf.com\/content_ICCV_2019\/html\/Peng_Moment_Matching_for_Multi-Source_Domain_Adaptation_ICCV_2019_paper.html. Accessed 19 July 2023.","DOI":"10.1109\/ICCV.2019.00149"},{"key":"851_CR81","doi-asserted-by":"publisher","unstructured":"Xiao J, Hays J, Ehinger KA, Oliva A, Torralba A. SUN database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE computer society conference on computer vision and pattern recognition; 2010. p. 3485\u201392 . https:\/\/doi.org\/10.1109\/CVPR.2010.5539970. ISSN: 1063-6919.","DOI":"10.1109\/CVPR.2010.5539970"},{"issue":"9","key":"851_CR82","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2019","unstructured":"Xian Y, Lampert CH, Schiele B, Akata Z. Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans Pattern Anal Mach Intell. 2019;41(9):2251\u201365. https:\/\/doi.org\/10.1109\/TPAMI.2018.2857768.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"851_CR83","doi-asserted-by":"publisher","unstructured":"Skorokhodov I, Elhoseiny M. Class normalization for (continual)? Generalized zero-shot learning; 2021. arXiv. arXiv:2006.11328 [cs, stat]. https:\/\/doi.org\/10.48550\/arXiv.2006.11328. http:\/\/arxiv.org\/abs\/2006.11328. Accessed 06 June 2023.","DOI":"10.48550\/arXiv.2006.11328"},{"key":"851_CR84","doi-asserted-by":"publisher","unstructured":"Varma G, Subramanian A, Namboodiri A, Chandraker M, Jawahar CV. IDD: a dataset for exploring problems of autonomous navigation in unconstrained environments. In: 2019 IEEE winter conference on applications of computer vision (WACV); 2019. p. 1743\u201351 https:\/\/doi.org\/10.1109\/WACV.2019.00190. ISSN: 1550-5790.","DOI":"10.1109\/WACV.2019.00190"},{"key":"851_CR85","doi-asserted-by":"publisher","unstructured":"Wang X, Huang TE, Darrell T, Gonzalez JE, Yu F. Frustratingly simple few-shot object detection; 2020. arXiv. arXiv:2003.06957 [cs]. https:\/\/doi.org\/10.48550\/arXiv.2003.06957. http:\/\/arxiv.org\/abs\/2003.06957. Accessed 02 June 2023.","DOI":"10.48550\/arXiv.2003.06957"},{"issue":"2","key":"851_CR86","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A. The pascal visual object classes (VOC) challenge. Int J Comput Vis. 2010;88(2):303\u201338. https:\/\/doi.org\/10.1007\/s11263-009-0275-4.","journal-title":"Int J Comput Vis"},{"key":"851_CR87","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.isprsjprs.2014.10.002","volume":"98","author":"G Cheng","year":"2014","unstructured":"Cheng G, Han J, Zhou P, Guo L. Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS J Photogrammetry Remote Sens. 2014;98:119\u201332. https:\/\/doi.org\/10.1016\/j.isprsjprs.2014.10.002.","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"851_CR88","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","volume":"159","author":"K Li","year":"2020","unstructured":"Li K, Wan G, Cheng G, Meng L, Han J. Object detection in optical remote sensing images: A survey and a new benchmark. ISPRS J Photogrammetry Remote Sens. 2020;159:296\u2013307. https:\/\/doi.org\/10.1016\/j.isprsjprs.2019.11.023.","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"851_CR89","doi-asserted-by":"publisher","unstructured":"Wang K, Liew JH, Zou Y, Zhou D, Feng J. PANet: few-shot image semantic segmentation with prototype alignment. In: 2019 IEEE\/CVF international conference on computer vision (ICCV); 2019. p. 9196\u2013205. https:\/\/doi.org\/10.1109\/ICCV.2019.00929. ISSN: 2380-7504.","DOI":"10.1109\/ICCV.2019.00929"},{"key":"851_CR90","doi-asserted-by":"crossref","unstructured":"Shrivastava A, Gupta A, Girshick R. Training region-based object detectors with online hard example mining; 2016. p. 761\u20139 . https:\/\/www.cv-foundation.org\/openaccess\/content_cvpr_2016\/html\/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.html. Accessed 26 May 2023.","DOI":"10.1109\/CVPR.2016.89"},{"key":"851_CR91","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T. 2015; U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention\u2014MICCAI 2015. Lecture notes in computer science. Cham: Springer. p. 234\u201341. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"851_CR92","doi-asserted-by":"publisher","unstructured":"Khashabi D, Min S, Khot T, Sabharwal A, Tafjord O, Clark P, Hajishirzi H. UNIFIEDQA: crossing format boundaries with a single qa system. in: findings of the association for computational linguistics: EMNLP. Association for Computational Linguistics, Online 2020; 2020. p. 1896\u2013907. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.171. https:\/\/aclanthology.org\/2020.findings-emnlp.171. Accessed 19 May 2023.","DOI":"10.18653\/v1\/2020.findings-emnlp.171"},{"key":"851_CR93","volume-title":"A survey of few-shot learning: an effective method for intrusion detection. Security and communication networks","author":"R Duan","year":"2021","unstructured":"Duan R, Li D, Tong Q, Yang T, Liu X, Liu X. A survey of few-shot learning: an effective method for intrusion detection. Security and communication networks. Hindawi Limited; 2021."},{"key":"851_CR94","doi-asserted-by":"publisher","unstructured":"Bansal A, Goldblum M, Cherepanova V, Schwarzschild A, Bruss CB, Goldstein T. MetaBalance: high-performance neural networks for class-imbalanced data; 2021. arXiv preprint arXiv:2106.09643. https:\/\/doi.org\/10.48550\/arxiv.2106.09643","DOI":"10.48550\/arxiv.2106.09643"},{"key":"851_CR95","doi-asserted-by":"crossref","unstructured":"Zhu L, Yang Y. Inflated episodic memory with region self-attention for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2020. p. 4344\u201353.","DOI":"10.1109\/CVPR42600.2020.00440"},{"key":"851_CR96","doi-asserted-by":"publisher","unstructured":"Samuel D, Atzmon Y, Chechik G. From generalized zero-shot learning to long-tail with class descriptors. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision; 2021. p. 286\u201395 . https:\/\/doi.org\/10.1109\/wacv48630.2021.00033.","DOI":"10.1109\/wacv48630.2021.00033"},{"issue":"4","key":"851_CR97","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1109\/jbhi.2020.2973372","volume":"24","author":"A Patra","year":"2020","unstructured":"Patra A, Noble JA. Hierarchical class incremental learning of anatomical structures in fetal echocardiography videos. IEEE J Biomed Health Inform. 2020;24(4):1046\u201358. https:\/\/doi.org\/10.1109\/jbhi.2020.2973372.","journal-title":"IEEE J Biomed Health Inform"},{"key":"851_CR98","doi-asserted-by":"publisher","unstructured":"Guan J, Liu J, Sun J, Feng P, Shuai T, Wang W. Meta metric learning for highly imbalanced aerial scene classification. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE; 2020. p. 4047\u201351. https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9052900","DOI":"10.1109\/ICASSP40776.2020.9052900"},{"key":"851_CR99","first-page":"415","volume-title":"Machine learning for health","author":"W-H Weng","year":"2020","unstructured":"Weng W-H, Deaton J, Natarajan V, Elsayed GF, Liu Y. Addressing the real-world class imbalance problem in dermatology. In: Machine learning for health. PMLR; 2020. p. 415\u201329."},{"key":"851_CR100","doi-asserted-by":"crossref","unstructured":"Dong N, Kampffmeyer M, Voiculescu I. learning underrepresented classes from decentralized partially labeled medical images. In: International conference on medical image computing and computer-assisted intervention. Springer; 2022. p. 67\u201376.","DOI":"10.1007\/978-3-031-16452-1_7"},{"issue":"8","key":"851_CR101","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/abe5e3","volume":"32","author":"Z Pei","year":"2021","unstructured":"Pei Z, Jiang H, Li X, Zhang J, Liu S. Data augmentation for rolling bearing fault diagnosis using an enhanced few-shot Wasserstein auto-encoder with meta-learning. Meas Sci Technol. 2021;32(8): 084007. https:\/\/doi.org\/10.1088\/1361-6501\/abe5e3.","journal-title":"Meas Sci Technol"},{"issue":"1","key":"851_CR102","doi-asserted-by":"publisher","first-page":"14820","DOI":"10.1038\/s41598-022-18986-z","volume":"12","author":"X Liu","year":"2022","unstructured":"Liu X, Gao W, Li R, Xiong Y, Tang X, Chen S. One shot ancient character recognition with siamese similarity network. Sci Rep. 2022;12(1):14820. https:\/\/doi.org\/10.1038\/s41598-022-18986-z.","journal-title":"Sci Rep"},{"key":"851_CR103","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104381","volume":"141","author":"Z Cui","year":"2022","unstructured":"Cui Z, Wang Q, Guo J, Lu N. Few-shot classification of fa\u00e7ade defects based on extensible classifier and contrastive learning. Autom Constr. 2022;141: 104381.","journal-title":"Autom Constr"},{"key":"851_CR104","doi-asserted-by":"publisher","first-page":"932","DOI":"10.3233\/SHTI230312","volume":"302","author":"O Sumer","year":"2023","unstructured":"Sumer O, Hellmann F, Hustinx A, Hsieh T-C, Andr\u00e9 E, Krawitz P. Few-shot meta-learning for recognizing facial phenotypes of genetic disorders. Stud Health Technol Inform. 2023;302:932\u20136. https:\/\/doi.org\/10.3233\/SHTI230312.","journal-title":"Stud Health Technol Inform"},{"key":"851_CR105","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2022.103628","volume":"138","author":"Z Zhan","year":"2022","unstructured":"Zhan Z, Zhou J, Xu B. Fabric defect classification using prototypical network of few-shot learning algorithm. Comput Ind. 2022;138: 103628.","journal-title":"Comput Ind"},{"key":"851_CR106","doi-asserted-by":"publisher","unstructured":"Tambwekar A, Agrawal K, Majee A, Subramanian A. Few-shot batch incremental road object detection via detector fusion. In: Proceedings of the IEEE\/CVF international conference on computer vision; 2021. p. 3070\u20137 . https:\/\/doi.org\/10.1109\/iccvw54120.2021.00341","DOI":"10.1109\/iccvw54120.2021.00341"},{"key":"851_CR107","doi-asserted-by":"publisher","unstructured":"Tian Y, Maicas G, Pu LZCT, Singh R, Verjans JW, Carneiro G. Few-shot anomaly detection for polyp frames from colonoscopy. In: Medical image computing and computer assisted intervention-MICCAI 2020: , Lima, Peru, October 4-8, 2020, Proceedings, Part VI 23. Springer; 2020. p. 274\u201384. https:\/\/doi.org\/10.48550\/arxiv.2006.14811","DOI":"10.48550\/arxiv.2006.14811"},{"key":"851_CR108","doi-asserted-by":"publisher","first-page":"1648","DOI":"10.1109\/TIP.2020.3046861","volume":"30","author":"Z Ji","year":"2021","unstructured":"Ji Z, Liu X, Pang Y, Ouyang W, Li X. Few-shot human-object interaction recognition with Semantic-guided attentive prototypes network. IEEE Trans Image Process Publ IEEE Signal Process Soc. 2021;30:1648\u201361. https:\/\/doi.org\/10.1109\/TIP.2020.3046861.","journal-title":"IEEE Trans Image Process Publ IEEE Signal Process Soc"},{"key":"851_CR109","doi-asserted-by":"publisher","unstructured":"Sharma R, Sharma G, Pattanaik M. A few shot learning based approach for hardware trojan detection using deep siamese cnn. In: 2021 34th international conference on VLSI design and 2021 20th international conference on embedded systems (VLSID). IEEE; 2021. p. 163\u20138. https:\/\/doi.org\/10.1109\/VLSID51830.2021.00033","DOI":"10.1109\/VLSID51830.2021.00033"},{"key":"851_CR110","doi-asserted-by":"publisher","unstructured":"Hu X, Jiang Y, Tang K, Chen J, Miao C, Zhang H. Learning to segment the tail. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2020. p. 14045\u201354. https:\/\/doi.org\/10.1109\/cvpr42600.2020.01406","DOI":"10.1109\/cvpr42600.2020.01406"},{"key":"851_CR111","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.ins.2023.03.105","volume":"634","author":"X Chen","year":"2023","unstructured":"Chen X, Zheng X, Sun K, Liu W, Zhang Y. Self-supervised vision transformer-based few-shot learning for facial expression recognition. Inf Sci. 2023;634:206\u201326. https:\/\/doi.org\/10.1016\/j.ins.2023.03.105.","journal-title":"Inf Sci"},{"key":"851_CR112","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1109\/LSP.2022.3168195","volume":"29","author":"C He","year":"2022","unstructured":"He C, Zhang J, Yao J, Zhuo L, Tian Q. Meta-Learning Paradigm and CosAttn for Streamer Action Recognition in Live Video. IEEE Signal Process Lett. 2022;29:1097\u2013101.","journal-title":"IEEE Signal Process Lett"},{"key":"851_CR113","doi-asserted-by":"publisher","unstructured":"Romanov S, Song H, Valstar M, Sharkey D, Henry C, Triguero I, Torres MT. Few-shot learning for postnatal gestational age estimation. In: 2021 international joint conference on neural networks (IJCNN). IEEE; 2021. p. 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9534239","DOI":"10.1109\/IJCNN52387.2021.9534239"},{"issue":"8","key":"851_CR114","doi-asserted-by":"publisher","first-page":"6894","DOI":"10.1109\/TGRS.2020.3032528","volume":"59","author":"Y Shi","year":"2020","unstructured":"Shi Y, Li J, Li Y, Du Q. Sensor-independent hyperspectral target detection with semisupervised domain adaptive few-shot learning. IEEE Trans Geosci Remote Sens. 2020;59(8):6894\u2013906. https:\/\/doi.org\/10.1109\/TGRS.2020.3032528.","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"851_CR115","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1007\/s10489-020-01886-y","volume":"51","author":"P Bedi","year":"2021","unstructured":"Bedi P, Gupta N, Jindal V. I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems. Appl Intell. 2021;51:1133\u201351. https:\/\/doi.org\/10.1007\/s10489-020-01886-y.","journal-title":"Appl Intell"},{"key":"851_CR116","doi-asserted-by":"publisher","unstructured":"Huang S, Liu Y, Fung C, An W, He R, Zhao Y, Yang H, Luan Z. a gated few-shot learning model for anomaly detection. In: 2020 international conference on information networking (ICOIN); 2020. p. 505\u20139 . https:\/\/doi.org\/10.1109\/ICOIN48656.2020.9016599. ISSN: 1976-7684","DOI":"10.1109\/ICOIN48656.2020.9016599"},{"key":"851_CR117","doi-asserted-by":"publisher","unstructured":"Gesi J, Li J, Ahmed I. An empirical examination of the impact of bias on just-in-time defect prediction. In: Proceedings of the 15th ACM\/IEEE international symposium on empirical software engineering and measurement (ESEM); 2021. p. 1\u201312. https:\/\/doi.org\/10.1145\/3475716.3475791","DOI":"10.1145\/3475716.3475791"},{"issue":"7","key":"851_CR118","doi-asserted-by":"publisher","first-page":"936","DOI":"10.3390\/e24070936","volume":"24","author":"X Wu","year":"2022","unstructured":"Wu X, Wang N. Detecting errors with zero-shot learning. Entropy (Basel, Switzerland). 2022;24(7):936. https:\/\/doi.org\/10.3390\/e24070936.","journal-title":"Entropy (Basel, Switzerland)"},{"key":"851_CR119","doi-asserted-by":"crossref","unstructured":"Li M, Zhang Y, Han D, Zhou M. Meta-IP: an imbalanced processing model based on meta-learning for IT project extension forecasts. Mathematical problems in engineering; 2022. https:\/\/www.hindawi.com\/journals\/mpe\/2022\/3140301\/.","DOI":"10.1155\/2022\/3140301"},{"key":"851_CR120","doi-asserted-by":"publisher","unstructured":"Chen X, Zhang C, Lin G, Han J. Compositional prototype network with multi-view comparision for few-shot point cloud semantic segmentation; 2020. arXiv preprint arXiv:2012.14255https:\/\/doi.org\/10.48550\/arxiv.2012.14255","DOI":"10.48550\/arxiv.2012.14255"},{"key":"851_CR121","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2023.3263219","author":"A Gao","year":"2023","unstructured":"Gao A, Mei F, Zheng J, Sha H, Guo M, Xie Y. Electricity theft detection based on contrastive learning and non-intrusive load monitoring. IEEE Trans Smart Grid. 2023. https:\/\/doi.org\/10.1109\/TSG.2023.3263219.","journal-title":"IEEE Trans Smart Grid"},{"key":"851_CR122","doi-asserted-by":"publisher","unstructured":"Gupta P, Bhaskarpandit S, Gupta M. Similarity Learning based few shot learning for ECG time series classification. In: 2021 digital image computing: techniques and applications (DICTA). IEEE; 2021. p. 1\u20138. https:\/\/doi.org\/10.1109\/DICTA52665.2021.9647357","DOI":"10.1109\/DICTA52665.2021.9647357"},{"key":"851_CR123","doi-asserted-by":"publisher","unstructured":"Bhosale S, Tiwari U, Chakraborty R, Kopparapu SK. Contrastive learning of cough descriptors for automatic COVID-19 preliminary diagnosis. Interspeech; 2021. https:\/\/doi.org\/10.21437\/Interspeech.2021-1249","DOI":"10.21437\/Interspeech.2021-1249"},{"key":"851_CR124","doi-asserted-by":"publisher","unstructured":"Renter\u00eda S, Vallejo EE, Taylor CE. Birdsong phrase verification and classification using siamese neural networks. bioRxiv. Cold Spring Harbor Laboratory. 2021\u201303; 2021. https:\/\/doi.org\/10.1101\/2021.03.16.435625.","DOI":"10.1101\/2021.03.16.435625"},{"key":"851_CR125","doi-asserted-by":"publisher","unstructured":"Sunder V, Fosler-Lussier E. Handling class imbalance in low-resource dialogue systems by combining few-shot classification and interpolation. In: ICASSP 2021-2021 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE; 2021. p. 7633\u20137. https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9413405","DOI":"10.1109\/ICASSP39728.2021.9413405"},{"issue":"16","key":"851_CR126","doi-asserted-by":"publisher","first-page":"11086","DOI":"10.1021\/acsomega.1c01266","volume":"6","author":"D Fern\u00e1ndez-Llaneza","year":"2021","unstructured":"Fern\u00e1ndez-Llaneza D, Ulander S, Gogishvili D, Nittinger E, Zhao H, Tyrchan C. Siamese Recurrent neural network with a self-attention mechanism for bioactivity prediction. ACS Omega. 2021;6(16):11086\u201394. https:\/\/doi.org\/10.1021\/acsomega.1c01266.","journal-title":"ACS Omega"},{"key":"851_CR127","unstructured":"Wenjuan G, Zhang Y, Wang W, Cheng P, Sabate JG. Meta-MMFNet: meta-learning based multi-model fusion network for micro-expression recognition. ACM transactions on multimedia computing, communications and applications. Association for Computing Machinery (ACM); 2022."},{"issue":"2","key":"851_CR128","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1007\/s10664-019-09779-6","volume":"25","author":"S Patil","year":"2020","unstructured":"Patil S, Ravindran B. Predicting software defect type using concept-based classification. Empir Softw Eng. 2020;25(2):1341\u201378. https:\/\/doi.org\/10.1007\/s10664-019-09779-6.","journal-title":"Empir Softw Eng"},{"key":"851_CR129","doi-asserted-by":"publisher","unstructured":"Tolstikhin I, Bousquet O, Gelly S, Schoelkopf B. Wasserstein auto-encoders; 2019. arXiv. arXiv:1711.01558 [cs, stat]. https:\/\/doi.org\/10.48550\/arXiv.1711.01558. http:\/\/arxiv.org\/abs\/1711.01558. Accessed 16 Aug 2023.","DOI":"10.48550\/arXiv.1711.01558"},{"key":"851_CR130","doi-asserted-by":"publisher","unstructured":"Nichol A, Achiam J, Schulman J. On first-order meta-learning algorithms; 2018. arXiv. arXiv:1803.02999 [cs]. https:\/\/doi.org\/10.48550\/arXiv.1803.02999. http:\/\/arxiv.org\/abs\/1803.02999. Accessed 16 June 2023.","DOI":"10.48550\/arXiv.1803.02999"},{"key":"851_CR131","doi-asserted-by":"crossref","unstructured":"Deng J, Guo J, Xue N, Zafeiriou S. ArcFace: additive angular margin loss for deep face recognition; 2019. p. 4690\u20139. https:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.html. Accessed 26 July 2023.","DOI":"10.1109\/CVPR.2019.00482"},{"key":"851_CR132","doi-asserted-by":"publisher","unstructured":"Fan Q, Zhuo W, Tang C-K, Tai Y-W. Few-shot object detection with attention-rpn and multi-relation detector; 2020. arXiv. arXiv:1908.01998 [cs]. https:\/\/doi.org\/10.48550\/arXiv.1908.01998. http:\/\/arxiv.org\/abs\/1908.01998. Accessed 18 June 2023.","DOI":"10.48550\/arXiv.1908.01998"},{"key":"851_CR133","doi-asserted-by":"publisher","unstructured":"Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks; 2016. arXiv. arXiv:1506.01497 [cs]. https:\/\/doi.org\/10.48550\/arXiv.1506.01497. http:\/\/arxiv.org\/abs\/1506.01497. Accessed 18 June 2023.","DOI":"10.48550\/arXiv.1506.01497"},{"key":"851_CR134","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer vision\u2013ECCV 2014 Lecture notes in computer science","author":"T-Y Lin","year":"2014","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL. Microsoft COCO: common objects in context. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T, editors. Computer vision\u2013ECCV 2014 Lecture notes in computer science. Cham: Springer; 2014. p. 740\u201355. https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48."},{"key":"851_CR135","doi-asserted-by":"crossref","unstructured":"Gupta A, Dollar P, Girshick R. LVIS: a dataset for large vocabulary instance segmentation; 2019. p. 5356\u201364. https:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Gupta_LVIS_A_Dataset_for_Large_Vocabulary_Instance_Segmentation_CVPR_2019_paper.html. Accessed 25 July 2023.","DOI":"10.1109\/CVPR.2019.00550"},{"key":"851_CR136","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X. Deep learning face attributes in the wild. In: Proceedings of international conference on computer vision (ICCV); 2015. https:\/\/openaccess.thecvf.com\/content_iccv_2015\/papers\/Liu_Deep_Learning_Face_ICCV_2015_paper.pdf.","DOI":"10.1109\/ICCV.2015.425"},{"key":"851_CR137","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1016\/j.procs.2020.04.085","volume":"171","author":"P Bedi","year":"2020","unstructured":"Bedi P, Gupta N, Jindal V. Siam-IDS: handling class imbalance problem in intrusion detection systems using siamese neural network. Procedia Comput Sci. 2020;171:780\u20139. https:\/\/doi.org\/10.1016\/j.procs.2020.04.085.","journal-title":"Procedia Comput Sci"},{"key":"851_CR138","doi-asserted-by":"publisher","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA. A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications; 2009. p. 1\u20136. https:\/\/doi.org\/10.1109\/CISDA.2009.5356528. ISSN: 2329-6275","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"851_CR139","doi-asserted-by":"publisher","unstructured":"Hoang T, Khanh\u00a0Dam H, Kamei Y, Lo D, Ubayashi N. DeepJIT: an end-to-end deep learning framework for just-in-time defect prediction. In: 2019 IEEE\/ACM 16th international conference on mining software repositories (MSR); 2019. p. 34\u201345. https:\/\/doi.org\/10.1109\/MSR.2019.00016. ISSN: 2574-3864.","DOI":"10.1109\/MSR.2019.00016"},{"key":"851_CR140","doi-asserted-by":"publisher","unstructured":"Heidari A, McGrath J, Ilyas IF, Rekatsinas T. HoloDetect: few-shot learning for error detection. In: Proceedings of the 2019 international conference on management of data. SIGMOD \u201919. Association for Computing Machinery, New York, NY, USA 2019. p. 829\u2013846. https:\/\/doi.org\/10.1145\/3299869.3319888. https:\/\/dl.acm.org\/doi\/10.1145\/3299869.3319888. Accessed 12 June 2023.","DOI":"10.1145\/3299869.3319888"},{"issue":"1","key":"851_CR141","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s40537-021-00514-x","volume":"8","author":"N Seliya","year":"2021","unstructured":"Seliya N, Abdollah Zadeh A, Khoshgoftaar TM. A literature review on one-class classification and its potential applications in big data. J Big Data. 2021;8(1):122. https:\/\/doi.org\/10.1186\/s40537-021-00514-x.","journal-title":"J Big Data"},{"issue":"2","key":"851_CR142","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1459352.1459355","volume":"41","author":"R Navigli","year":"2009","unstructured":"Navigli R. Word sense disambiguation: a survey. ACM Comput Surv. 2009;41(2):10\u201311069. https:\/\/doi.org\/10.1145\/1459352.1459355.","journal-title":"ACM Comput Surv"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00851-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-023-00851-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00851-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T17:10:59Z","timestamp":1704215459000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-023-00851-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,2]]},"references-count":142,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["851"],"URL":"https:\/\/doi.org\/10.1186\/s40537-023-00851-z","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,2]]},"assertion":[{"value":"23 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1"}}