{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T21:05:25Z","timestamp":1783976725894,"version":"3.55.0"},"reference-count":27,"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":"Entropy Cluster at the Institute of Informatics, University of Warsaw"},{"name":"NVIDIA, Intel, the Polish National Science Center","award":["UMO2017\/26\/E\/ST6\/00622"],"award-info":[{"award-number":["UMO2017\/26\/E\/ST6\/00622"]}]},{"DOI":"10.13039\/501100000781","name":"European Research Council (ERC) Starting Grant TOTAL","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3704899","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T19:39:49Z","timestamp":1781725189000},"page":"101774-101789","source":"Crossref","is-referenced-by-count":0,"title":["<i>n<\/i>\n                    -CPS: Generalising Cross Pseudo Supervision to\n                    <i>n<\/i>\n                    Networks"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4927-9992","authenticated-orcid":false,"given":"Dominik","family":"Filipiak","sequence":"first","affiliation":[{"name":"AI Clearing Inc., Austin, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piotr","family":"Tempczyk","sequence":"additional","affiliation":[{"name":"AI Clearing Inc., Austin, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marek","family":"Cygan","sequence":"additional","affiliation":[{"name":"Institute of Informatics, University of Warsaw, Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01471"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.5244\/C.34.154"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-019-8208-z"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58601-0_26"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.02611"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.89"},{"key":"ref12","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00299"},{"key":"ref15","first-page":"40367","article-title":"Switching temporary teachers for semi-supervised semantic segmentation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Chang"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/WACV61042.2026.00481"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00348"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3528453"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2983686"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108777"},{"key":"ref25","article-title":"Bootstrapping semantic segmentation with regional contrast","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Liu"},{"key":"ref26","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"Sohn"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74958-5_42"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11569951.pdf?arnumber=11569951","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T20:05:47Z","timestamp":1783973147000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11569951\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3704899","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}