{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:43:47Z","timestamp":1778694227025,"version":"3.51.4"},"reference-count":49,"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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tgrs.2024.3438844","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T17:42:44Z","timestamp":1722879764000},"page":"1-13","source":"Crossref","is-referenced-by-count":6,"title":["Global Focal Learning for Semi-Supervised Oriented Object Detection"],"prefix":"10.1109","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7653-9120","authenticated-orcid":false,"given":"Kai","family":"Wang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8239-2268","authenticated-orcid":false,"given":"Zhifeng","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7867-7394","authenticated-orcid":false,"given":"Qiao","family":"Wan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5314-3476","authenticated-orcid":false,"given":"Fanfan","family":"Xia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2434-6860","authenticated-orcid":false,"given":"Pin","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deren","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering and the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3051383"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2022.12.004"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3301854"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3046647"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2023.01.011"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00477"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20077-9_3"},{"key":"ref8","first-page":"1","article-title":"Unbiased teacher for semi-supervised object detection","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Liu"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00305"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01493"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2022.3220219"},{"key":"ref12","first-page":"1","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Laine"},{"key":"ref13","first-page":"1","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":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00729"},{"key":"ref15","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Sohn"},{"key":"ref16","first-page":"1","article-title":"Freematch: Self-adaptive thresholding for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref17","first-page":"1","article-title":"Softmatch: Addressing the quantity-quality tradeoff in semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref18","article-title":"A simple semi-supervised learning framework for object detection","author":"Sohn","year":"2020","journal-title":"arXiv:2005.04757"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00407"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20077-9_27"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i3.25455"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00959"},{"key":"ref23","article-title":"Rethinking scale imbalance in semi-supervised object detection for aerial images","author":"Zhang","year":"2023","journal-title":"arXiv:2310.14718"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2024.3380645"},{"key":"ref25","article-title":"Density-guided dense Pseudo label selection for semi-supervised oriented object detection","author":"Zhao","year":"2023","journal-title":"arXiv:2311.12608"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2974745"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2020.09.022"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_40"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2981203"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2021.3062048"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3148874"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2023.3264204"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i3.16336"},{"key":"ref39","first-page":"11830","article-title":"Rethinking rotated object detection with Gaussian Wasserstein distance loss","volume-title":"Proc. 38th Int. Conf. Mach. Learn. (ICML)","author":"Yang"},{"key":"ref40","first-page":"18381","article-title":"Learning high-precision bounding box for rotated object detection via Kullback\u2013Leibler divergence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Yang"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58558-7_12"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref43","article-title":"Anchor-free oriented proposal generator for object detection","author":"Cheng","year":"2021","journal-title":"arXiv:2110.01931"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2601622"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2014.999881"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.12.004"},{"key":"ref47","doi-asserted-by":"crossref","DOI":"10.1145\/3503161.3548541","volume-title":"Mmrotate: A Rotated Object Detection Benchmark Using Pytorch","author":"Zhou","year":"2022"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2021.3138933"},{"key":"ref49","first-page":"1","article-title":"Consistency-based semi-supervised learning for object detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Jeong"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/36\/10354519\/10623510.pdf?arnumber=10623510","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T05:27:34Z","timestamp":1724131654000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10623510\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2024.3438844","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}