{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:44:21Z","timestamp":1775745861248,"version":"3.50.1"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFE0204200"],"award-info":[{"award-number":["2021YFE0204200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62101378"],"award-info":[{"award-number":["62101378"]}],"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":["62171318"],"award-info":[{"award-number":["62171318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M732612"],"award-info":[{"award-number":["2023M732612"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tim.2023.3328079","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T17:50:54Z","timestamp":1698429054000},"page":"1-14","source":"Crossref","is-referenced-by-count":8,"title":["Few-Shot Omnidirectional Human Motion Recognition Using Monostatic Radar System"],"prefix":"10.1109","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1364-8653","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0368-372X","authenticated-orcid":false,"given":"Junhan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3621-0478","authenticated-orcid":false,"given":"Beichen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8160-2999","authenticated-orcid":false,"given":"Yue","family":"Lang","sequence":"additional","affiliation":[{"name":"School of Electric and Information Engineering, Hebei University of Technology, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7260-9099","authenticated-orcid":false,"given":"Wenguang","family":"Zhai","sequence":"additional","affiliation":[{"name":"Tianjin 764 Communication Navigation Technology Company Ltd., Tianjin, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2019.8856974"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/EuRAD48048.2021.00103"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/BMEiCON47515.2019.8990358"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2018.04.007"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108252"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2018.2890128"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2015.2439393"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2016.140682"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2908758"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3214271"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3225060"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2958178"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2903715"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.11.109"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2020.3036637"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1049\/icp.2021.0555"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/RADAR42522.2020.9114613"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.2971626"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/RadarConf2147009.2021.9454972"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1049\/icp.2021.0651"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2015.2491329"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2018.2799758"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/s16121990"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2919770"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2019.01.013"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/7541814"},{"key":"ref27","article-title":"Data augmentation generative adversarial networks","author":"Antoniou","year":"2017","journal-title":"arXiv:1711.04340"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01052"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.328"},{"key":"ref30","first-page":"2554","article-title":"Meta networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Munkhdalai"},{"key":"ref31","first-page":"3664","article-title":"Rapid adaptation with conditionally shifted neurons","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Munkhdalai"},{"key":"ref32","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref33","article-title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","author":"Triantafillou","year":"2019","journal-title":"arXiv:1903.03096"},{"key":"ref34","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Santoro"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref36","first-page":"1","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Vinyals"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICEIEC49280.2020.9152261"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00419"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00743"},{"key":"ref41","article-title":"Transferability in deep learning: A survey","author":"Jiang","year":"2022","journal-title":"arXiv:2201.05867"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018642"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00338"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00621"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3164083"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/100"},{"key":"ref48","article-title":"Meta-learning with differentiable closed-form solvers","author":"Bertinetto","year":"2018","journal-title":"arXiv:1805.08136"},{"key":"ref49","article-title":"Meta-learning probabilistic inference for prediction","author":"Gordon","year":"2018","journal-title":"arXiv:1805.09921"},{"key":"ref50","first-page":"1","article-title":"SVCCA: Singular vector canonical correlation analysis for deep learning dynamics and interpretability","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Raghu"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/19\/10012124\/10298273.pdf?arnumber=10298273","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T11:21:39Z","timestamp":1709378499000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10298273\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/tim.2023.3328079","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}