{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:35:36Z","timestamp":1778258136565,"version":"3.51.4"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":["42192580"],"award-info":[{"award-number":["42192580"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42192583"],"award-info":[{"award-number":["42192583"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Hubei Province Natural Science Foundation","doi-asserted-by":"publisher","award":["2021CFA088"],"award-info":[{"award-number":["2021CFA088"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Hubei Province Natural Science Foundation","doi-asserted-by":"publisher","award":["2020CFA003"],"award-info":[{"award-number":["2020CFA003"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"Science and Technology Major Project","doi-asserted-by":"publisher","award":["2021AAA010"],"award-info":[{"award-number":["2021AAA010"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"Science and Technology Major Project","doi-asserted-by":"publisher","award":["2021AAA010-3"],"award-info":[{"award-number":["2021AAA010-3"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chinese Association For Artificial Intelligence (CAAI)-Huawei MindSpore Open Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tgrs.2022.3184080","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T19:26:09Z","timestamp":1655493969000},"page":"1-13","source":"Crossref","is-referenced-by-count":34,"title":["Few-Shot Scene Classification of Optical Remote Sensing Images Leveraging Calibrated Pretext Tasks"],"prefix":"10.1109","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0812-4334","authenticated-orcid":false,"given":"Hong","family":"Ji","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3325-1183","authenticated-orcid":false,"given":"Zhi","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9845-4251","authenticated-orcid":false,"given":"Yongjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1649-9611","authenticated-orcid":false,"given":"Yu","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6850-5721","authenticated-orcid":false,"given":"Can","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4166-7891","authenticated-orcid":false,"given":"Tiancan","family":"Mei","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan, China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref2","first-page":"1","article-title":"A closer look at few-shot classification","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00893"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3099033"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2675998"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2010.2055033"},{"key":"ref7","first-page":"2579","article-title":"What does rotation prediction tell us about classifier accuracy under varying testing environments?","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Deng"},{"key":"ref8","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3058249"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2933501"},{"key":"ref11","first-page":"10727","article-title":"Dropblock: A regularization method for convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Ghiasi"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00815"},{"key":"ref13","first-page":"1","article-title":"Unsupervised representation learning by predicting image rotations","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Gidaris"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref15","first-page":"15663","article-title":"Using self-supervised learning can improve model robustness and uncertainty","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Hendrycks"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.1.1"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/rs9090907"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2909541"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"ref21","first-page":"18661","article-title":"Supervised contrastive learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Khosla"},{"key":"ref22","first-page":"5714","article-title":"Self-supervised label augmentation via input transformations","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Lee"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3027387"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3033336"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3109268"},{"key":"ref26","article-title":"Meta-SGD: Learning to learn quickly for few-shot learning","author":"Li","year":"2017","journal-title":"arXiv:1707.09835"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2872830"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17047"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/418"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01226"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2018.2803784"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3048002"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093338"},{"key":"ref34","article-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018","journal-title":"arXiv:1803.02999"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"ref36","first-page":"719","article-title":"TADAM: Task dependent adaptive metric for improved few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Oreshkin"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86486-6_41"},{"key":"ref38","first-page":"1","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Ravi"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.4.013201"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.3390\/rs11111374"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00108"},{"key":"ref42","first-page":"1","article-title":"Meta-learning with latent embedding optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Rusu"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/BF00993504"},{"key":"ref44","first-page":"4080","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Snell"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.54"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"ref49","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref51","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Verma"},{"key":"ref52","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Vinyals"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3051024"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.320"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2685945"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2021.3109728"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2897652"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00156"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00931"},{"key":"ref60","first-page":"1","article-title":"IEPT: Instance-level and episode-level pretext tasks for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.3390\/rs13010108"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2496185"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00806"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.111838"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/9633014\/09799778.pdf?arnumber=9799778","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T04:38:45Z","timestamp":1706762325000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9799778\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":65,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2022.3184080","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}