{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:36:21Z","timestamp":1773930981934,"version":"3.50.1"},"reference-count":42,"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"}],"funder":[{"DOI":"10.13039\/501100003704","name":"Korea Research Institute for Defense Technology Planning and Advancement (KRIT) through the Korea Government","doi-asserted-by":"publisher","award":["R230206"],"award-info":[{"award-number":["R230206"]}],"id":[{"id":"10.13039\/501100003704","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tgrs.2024.3378311","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T18:20:27Z","timestamp":1710958827000},"page":"1-16","source":"Crossref","is-referenced-by-count":1,"title":["Fine-Grained Binary Object Segmentation in Remote Sensing Imagery via Path-Selective Test-Time Adaptation"],"prefix":"10.1109","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7223-0903","authenticated-orcid":false,"given":"Kyungsu","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Computer Science & Artificial Intelligence, Jeonbuk National University, Jeonju, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7572-1705","authenticated-orcid":false,"given":"Haeyun","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Korea University of Technology & Education, Cheonan, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9059-1291","authenticated-orcid":false,"given":"Juhum","family":"Park","sequence":"additional","affiliation":[{"name":"Dabeeo Inc., Seoul, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4659-6009","authenticated-orcid":false,"given":"Jae Youn","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, The Interdisciplinary Studies of Artificial Intelligence, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2307\/3235884"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03294-3_20"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1177\/0278364914549607"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.544"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2719738"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2868880"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.07.007"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.02.139"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/rs10030407"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3065112"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2018.8519409"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref17","article-title":"FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics","author":"Minh Quan","year":"2016","journal-title":"arXiv:1612.05360"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref19","article-title":"Hierarchical multi-scale attention for semantic segmentation","author":"Tao","year":"2020","journal-title":"arXiv:2005.10821"},{"key":"ref20","article-title":"Rethinking pre-training and self-training","author":"Zoph","year":"2020","journal-title":"arXiv:2006.06882"},{"key":"ref21","article-title":"Channelized axial attention for semantic segmentation","author":"Huang","year":"2021","journal-title":"arXiv:2101.07434"},{"key":"ref22","article-title":"BEiT: BERT pre-training of image transformers","author":"Bao","year":"2021","journal-title":"arXiv:2106.08254"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref24","article-title":"LadderNet: Multi-path networks based on U-Net for medical image segmentation","author":"Zhuang","year":"2018","journal-title":"arXiv:1810.07810"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3390\/app10186386"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102027"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/IAI.2000.839594"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0184290"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.243"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.28.3.033035"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref32","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":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2858817"},{"key":"ref36","volume-title":"Machine learning for aerial image labeling","author":"Mnih","year":"2013"},{"key":"ref37","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015","journal-title":"arXiv:1502.03167"},{"key":"ref38","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","volume-title":"Proc. 12th USENIX Symp. Oper. Syst. Design Implement.","author":"Abadi"},{"key":"ref39","volume-title":"Machine Learning: A Probabilistic Perspective","author":"Kevin","year":"2012"},{"key":"ref40","article-title":"UMAP: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018","journal-title":"arXiv:1802.03426"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.89"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/10354519\/10476498.pdf?arnumber=10476498","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T04:28:44Z","timestamp":1716006524000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10476498\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2024.3378311","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}