{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:08:46Z","timestamp":1790273326313,"version":"4.1.0"},"reference-count":33,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Ministry of Education Youth Fund Project for Humanities and Social Sciences Research","award":["24YJC760019"],"award-info":[{"award-number":["24YJC760019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3732225","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T19:06:48Z","timestamp":1788894408000},"page":"141084-141094","source":"Crossref","is-referenced-by-count":0,"title":["HCCL: Hierarchical Consistency-Driven Contrastive Learning for Semi-Supervised Learning"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0569-5864","authenticated-orcid":false,"given":"Tianzhu","family":"Cong","sequence":"first","affiliation":[{"name":"Jiangnan University","place":["Wuxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5160-4837","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Jiangnan University","place":["Wuxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5831-1475","authenticated-orcid":false,"given":"Hefeng","family":"Yin","sequence":"additional","affiliation":[{"name":"Wuxi University","place":["Wuxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"ReMixMatch: Semi-supervised learning with distribution alignment and augmentation anchoring","author":"Berthelot","year":"2019","journal-title":"arXiv:1911.09785"},{"key":"ref2","first-page":"5050","article-title":"Mixmatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Berthelot"},{"key":"ref3","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Caron"},{"key":"ref4","article-title":"Softmatch: Addressing the quantity-Quality trade-off in semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Chen"},{"key":"ref5","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. PMLR","author":"Chen"},{"key":"ref6","first-page":"22243","article-title":"Big self-supervised models are strong semi-supervised learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Chen"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3228380"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01723-4"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00212"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.111121"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2025.103013"},{"key":"ref14","first-page":"18661","article-title":"Supervised contrastive learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Khosla"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00934"},{"key":"ref16","article-title":"DINOv2: Learning robust visual features without supervision","author":"Oquab","year":"2024","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref17","first-page":"1","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vinyals"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2025.3535115"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-78192-6_15"},{"key":"ref20","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Sohn"},{"key":"ref21","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Tarvainen"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-025-11198-7"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43424-2_7"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0285"},{"key":"ref25","article-title":"Freematch: Self-adaptive thresholding for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Wang"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01513"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00461"},{"key":"ref29","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Xie"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01402"},{"key":"ref31","first-page":"18408","article-title":"FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Zhang"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10721-6"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01506"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11683734.pdf?arnumber=11683734","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:29:40Z","timestamp":1790270980000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11683734\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3732225","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}