{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:21:55Z","timestamp":1740169315687,"version":"3.37.3"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018AAA0100400"],"award-info":[{"award-number":["2018AAA0100400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MF131","ZR2021ZD19"],"award-info":[{"award-number":["ZR2020MF131","ZR2021ZD19"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Program of Qingdao","award":["21-1-4-ny-19-nsh"],"award-info":[{"award-number":["21-1-4-ny-19-nsh"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3199003","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T19:51:16Z","timestamp":1660593076000},"page":"86733-86743","source":"Crossref","is-referenced-by-count":1,"title":["Category Relevance Redirection Network for Few-Shot Classification"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1504-6875","authenticated-orcid":false,"given":"Xiangtao","family":"Tian","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5014-4668","authenticated-orcid":false,"given":"Zhengli","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Education, Ocean University of China, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1032-7774","authenticated-orcid":false,"given":"Peng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Network and Information Center, Ocean University of China, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6653-131X","authenticated-orcid":false,"given":"Jianxin","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2952-6642","authenticated-orcid":false,"given":"Guoqiang","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. 5th Int. Conf. Learn. Represent. (ICLR)","author":"Ravi","key":"ref2"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2021.102065"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.17021"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412145"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11018-5_63"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63820-7_48"},{"key":"ref9","article-title":"Meta-learning for semi-supervised few-shot classification","volume-title":"arXiv:1803.00676","author":"Ren","year":"2018"},{"key":"ref10","first-page":"1","article-title":"Unsupervised meta-learning for few-shot image classification","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Khodadadeh"},{"key":"ref11","article-title":"Meta-learning with latent embedding optimization","volume-title":"arXiv:1807.05960","author":"Rusu","year":"2018"},{"key":"ref12","article-title":"Neural Turing machines","volume-title":"arXiv:1410.5401","author":"Graves","year":"2014"},{"key":"ref13","first-page":"3825","article-title":"LGM-NET: Learning to generate matching networks for few-shot learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00459"},{"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":"Siamese neural networks for one-shot image recognition","volume-title":"Proc. ICML deep Learn. Workshop","volume":"2","author":"Koch"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_37"},{"key":"ref19","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013379"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01285"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_17"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"ref25","article-title":"Few-shot classification via adaptive attention","volume-title":"arXiv:2008.02465","author":"Jiang","year":"2020"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86486-6_41"},{"key":"ref27","first-page":"140","article-title":"Contrastive prototype learning with augmented embeddings for few-shot learning","volume-title":"Proc. 37th Conf. Uncertainty Artif. Intell.","author":"Gao"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/149"},{"key":"ref29","first-page":"1","article-title":"Cross attention network for few-shot classification","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Hou"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00097"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00870"},{"key":"ref32","article-title":"A simple neural attentive meta-learner","volume-title":"arXiv:1707.03141","author":"Mishra","year":"2017"},{"key":"ref33","article-title":"Rapid adaptation with conditionally shifted neurons","volume-title":"arXiv:1712.09926","author":"Munkhdalai","year":"2017"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref36","article-title":"A closer look at few-shot classification","volume-title":"arXiv:1904.04232","author":"Chen","year":"2019"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019079"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00888"},{"key":"ref39","first-page":"721","article-title":"TADAM: Task dependent adaptive metric for improved few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Oreshkin"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00042"},{"key":"ref41","article-title":"Learning to propagate labels: Transductive propagation network for few-shot learning","volume-title":"arXiv:1805.10002","author":"Liu","year":"2018"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00011"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref44","first-page":"4077","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Snell"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00948"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.09.070"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01348"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref49","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Krizhevsky"},{"issue":"4","key":"ref50","article-title":"Learning multiple layers of features from tiny images","volume":"1","author":"Krizhevsky","year":"2009","journal-title":"Handbook of Systemic Autoimmune Diseases"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09857894.pdf?arnumber=9857894","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T05:10:26Z","timestamp":1709356226000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9857894\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3199003","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2022]]}}}