{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T21:01:28Z","timestamp":1764277288431,"version":"3.37.3"},"reference-count":74,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015549","name":"Macau Science and Technology Development Fund","doi-asserted-by":"publisher","award":["2021YFA0716100"],"award-info":[{"award-number":["2021YFA0716100"]}],"id":[{"id":"10.13039\/100015549","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515110573"],"award-info":[{"award-number":["2022A1515110573"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF1200804"],"award-info":[{"award-number":["2021YFF1200804"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3462295","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T18:06:33Z","timestamp":1726509993000},"page":"133648-133669","source":"Crossref","is-referenced-by-count":3,"title":["ConvNeXt-ECA: An Effective Encoder Network for Few-Shot Learning"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7299-2106","authenticated-orcid":false,"given":"Cheng-Xue","family":"Lao","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ah","family":"Chung Tsoi","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1748-6372","authenticated-orcid":false,"given":"Roberto","family":"Bugiolacchi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1145\/3582688"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1002\/mp.15555"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1016\/j.comcom.2022.11.001"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref5","article-title":"Generalized neural collapse for a large number of classes","author":"Jiang","year":"2023","journal-title":"arXiv:2310.05351"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1007\/978-3-030-29407-6_17"},{"key":"ref7","article-title":"Improve supervised representation learning with masked image modeling","author":"Chen","year":"2023","journal-title":"arXiv:2312.00950"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/CVPR52688.2022.01426"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1109\/ICASSP49357.2023.10096887"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/TKDE.2009.191"},{"volume-title":"Constrained Optimization and Lagrange Multiplier Method","year":"1982","author":"Bertsekas","key":"ref11"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1007\/978-3-031-20044-1_30"},{"issue":"11","key":"ref13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11432-022-3700-8","article-title":"Sparse spatial transformers for few-shot learning","volume":"66","author":"Chen","year":"2023","journal-title":"Sci. China Inf. Sci."},{"key":"ref14","first-page":"1","article-title":"Siamese neural networks for one-shot image recognition","volume-title":"Proc. ICML Deep Learn. Workshop","author":"Koch"},{"key":"ref15","first-page":"1","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Vinyals"},{"key":"ref16","first-page":"1","article-title":"Imagenet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1109\/CVPR.2017.634"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref20","first-page":"1","article-title":"Tadam: Task dependent adaptive metric for improved few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Oreshkin"},{"key":"ref21","article-title":"Meta-learning with differentiable closed-form solvers","author":"Bertinetto","year":"2018","journal-title":"arXiv:1805.08136"},{"key":"ref22","article-title":"Meta-learning for semi-supervised few-shot classification","author":"Ren","year":"2018","journal-title":"arXiv:1803.00676"},{"volume-title":"The caltech-ucsd birds-200\u20132011 dataset","year":"2011","author":"Wah","key":"ref23"},{"key":"ref24","first-page":"1","article-title":"Sparse representations for fast, one-shot learning","volume-title":"Proc. Nat. Conf. Artif. Intell.","author":"Sussman"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1109\/ICCV.2003.1238476"},{"volume-title":"One-shot learning with Bayesian networks","year":"2009","author":"Maas","key":"ref26"},{"key":"ref27","first-page":"195","article-title":"One-shot learning with a hierarchical nonparametric Bayesian model","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"27","author":"Salakhutdinov"},{"issue":"2","key":"ref28","first-page":"1","article-title":"Few-shot object detection: A comprehensive survey","volume":"1","author":"K\u00f6hler","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref29","first-page":"1","article-title":"Delta-encoder: An effective sample synthesis method for few-shot object recognition","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Schwartz"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.1109\/ICEIEC49280.2020.9152261"},{"key":"ref31","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Finn"},{"key":"ref32","first-page":"21981","article-title":"Crosstransformers: Spatially-aware few-shot transfer","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Doersch"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.1109\/ICCV48922.2021.00870"},{"doi-asserted-by":"publisher","key":"ref34","DOI":"10.1109\/CVPR.2018.00131"},{"doi-asserted-by":"publisher","key":"ref35","DOI":"10.1109\/IJCNN48605.2020.9206909"},{"key":"ref36","first-page":"17429","article-title":"Discovering symbolic models from deep learning with inductive biases","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Cranmer"},{"doi-asserted-by":"publisher","key":"ref37","DOI":"10.1088\/1742-5468\/ac9830"},{"key":"ref38","first-page":"1","article-title":"On the inductive bias of neural tangent kernels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Bietti"},{"key":"ref39","article-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia","year":"2018","journal-title":"arXiv:1806.01261"},{"doi-asserted-by":"publisher","key":"ref40","DOI":"10.1145\/3293318"},{"doi-asserted-by":"publisher","key":"ref41","DOI":"10.1145\/3386252"},{"doi-asserted-by":"publisher","key":"ref42","DOI":"10.1016\/j.asoc.2020.106384"},{"doi-asserted-by":"publisher","key":"ref43","DOI":"10.1109\/CVPR52688.2022.01553"},{"doi-asserted-by":"publisher","key":"ref44","DOI":"10.1109\/CVPR.2016.572"},{"key":"ref45","first-page":"1","article-title":"Fractalnet: Ultra-deep neural networks without residuals","volume-title":"Proc. ICLR Int. Conf. Learn. Res.","author":"Larsson"},{"doi-asserted-by":"publisher","key":"ref46","DOI":"10.1007\/978-3-319-46493-0_39"},{"key":"ref47","first-page":"1","article-title":"Nextvlad: An efficient neural network to aggregate frame-level features for large-scale video classification","volume-title":"Proc. Eur. Conf. Comput. Vis. (ECCV) Workshops","author":"Lin"},{"doi-asserted-by":"publisher","key":"ref48","DOI":"10.1109\/CVPR.2010.5540039"},{"key":"ref49","first-page":"1","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dosovitskiy"},{"key":"ref50","first-page":"2902","article-title":"Large-scale evolution of image classifiers","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Real"},{"key":"ref51","article-title":"RandAugment: Practical automated data augmentation with a reduced search space","author":"Cubuk","year":"2019","journal-title":"arXiv:1909.13719"},{"key":"ref52","article-title":"Mixing in 3-D cavity by moving cavity walls","author":"Povitsky","year":"2018","journal-title":"arXiv:1807.04896"},{"doi-asserted-by":"publisher","key":"ref53","DOI":"10.48550\/arxiv.1710.09412"},{"doi-asserted-by":"publisher","key":"ref54","DOI":"10.1109\/ICCV.2019.00612"},{"doi-asserted-by":"publisher","key":"ref55","DOI":"10.1109\/ICCV48922.2021.00986"},{"doi-asserted-by":"publisher","key":"ref56","DOI":"10.1109\/CVPR42600.2020.01155"},{"doi-asserted-by":"publisher","key":"ref57","DOI":"10.1007\/s10489-022-03613-1"},{"doi-asserted-by":"publisher","key":"ref58","DOI":"10.1155\/2024\/1052344"},{"volume-title":"Cifar-100","year":"2009","author":"Krizhevsky","key":"ref59"},{"doi-asserted-by":"publisher","key":"ref60","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref61","first-page":"1","article-title":"Decoupled weight decay regularization","volume-title":"Proc. ICLR","author":"Loshchilov"},{"doi-asserted-by":"publisher","key":"ref62","DOI":"10.1109\/ICCV48922.2021.00010"},{"doi-asserted-by":"publisher","key":"ref63","DOI":"10.1109\/CVPR.2019.01091"},{"doi-asserted-by":"publisher","key":"ref64","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref65","first-page":"1","article-title":"A baseline for few-shot image classi cation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dhillon"},{"key":"ref66","article-title":"Few-shot adaptation of generative adversarial networks","author":"Robb","year":"2020","journal-title":"arXiv:2010.11943"},{"key":"ref67","first-page":"53","article-title":"A primal-dual link between gans and autoencoders","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst.","author":"Husain"},{"doi-asserted-by":"publisher","key":"ref68","DOI":"10.1109\/WACV45572.2020.9093338"},{"key":"ref69","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Verma"},{"doi-asserted-by":"publisher","key":"ref70","DOI":"10.1109\/ICCV48922.2021.00893"},{"key":"ref71","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-56927-2","volume-title":"Self Organizing Map","author":"Kohonen","year":"2001"},{"doi-asserted-by":"publisher","key":"ref72","DOI":"10.1109\/CVPR42600.2020.01026"},{"doi-asserted-by":"publisher","key":"ref73","DOI":"10.5244\/C.30.87"},{"doi-asserted-by":"publisher","key":"ref74","DOI":"10.1109\/CVPR52729.2023.01548"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10681083.pdf?arnumber=10681083","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T05:33:44Z","timestamp":1727933624000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10681083\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":74,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3462295","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2024]]}}}