{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T12:40:09Z","timestamp":1759236009505,"version":"3.44.0"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52305160"],"award-info":[{"award-number":["52305160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024M752206"],"award-info":[{"award-number":["2024M752206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tim.2025.3608327","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T17:45:38Z","timestamp":1757526338000},"page":"1-17","source":"Crossref","is-referenced-by-count":0,"title":["Combining Unsupervised Domain Adaptation and Semi-Supervised Learning for Power Line and Transmission Tower Segmentation"],"prefix":"10.1109","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5308-4813","authenticated-orcid":false,"given":"Gaoyi","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu, Sichuan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3389-1176","authenticated-orcid":false,"given":"Yong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu, Sichuan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3733-5353","authenticated-orcid":false,"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu, Sichuan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3966-7796","authenticated-orcid":false,"given":"Mei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu, Sichuan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5830-9946","authenticated-orcid":false,"given":"Lanxin","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu, Sichuan, China"}]},{"given":"Yiwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Wangjiang Hospital, Sichuan University, Chengdu, Sichuan, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2017.12.016"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3201499"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/rs10040613"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2022.102740"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3169555"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3145514"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IROS55552.2023.10341871"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116771"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120359"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121087"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3381713"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2022.116634"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2007.895714"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-009-0206-y"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICICEE.2012.77"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113895"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123773"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3325447"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.105181"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3488143"},{"key":"ref21","article-title":"Improved regularization of convolutional neural networks with CutOut","author":"DeVries","year":"2017","journal-title":"arXiv:1708.04552"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1710.09412"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00141"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2856256"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3101879"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3009075"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3281503"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109319"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110962"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2960224"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3228042"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3390\/rs11111342"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3157336"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3259047"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123936"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.02611"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-69544-6_36"},{"article-title":"SOLOv2: Dynamic and fast instance segmentation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang","key":"ref43"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00421"},{"key":"ref45","first-page":"22106","article-title":"Semi-supervised semantic segmentation via adaptive equalization learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hu"},{"key":"ref46","article-title":"Semi-supervised semantic segmentation via marginal contextual information","author":"Kimhi","year":"2023","journal-title":"arXiv:2308.13900"},{"key":"ref47","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"32","author":"Berthelot"},{"article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Berthelot","key":"ref48"},{"article-title":"FlexMatch: Boosting semi-supervised learning with curriculum pseudo labeling","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NEURIPS)","author":"Zhang","key":"ref49"},{"key":"ref50","article-title":"FreeMatch: Self-adaptive thresholding for semi-supervised learning","author":"Wang","year":"2022","journal-title":"arXiv:2205.07246"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02509"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02640"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3146744"},{"key":"ref56","first-page":"22233","article-title":"Dropout reduces underfitting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3388199"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3528453"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/19\/10764799\/11156116.pdf?arnumber=11156116","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T12:22:27Z","timestamp":1759234947000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11156116\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/tim.2025.3608327","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"type":"print","value":"0018-9456"},{"type":"electronic","value":"1557-9662"}],"subject":[],"published":{"date-parts":[[2025]]}}}