{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:39:08Z","timestamp":1773772748922,"version":"3.50.1"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T00:00:00Z","timestamp":1695513600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T00:00:00Z","timestamp":1695513600000},"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":[],"published-print":{"date-parts":[[2023,9,24]]},"DOI":"10.1109\/itsc57777.2023.10421807","type":"proceedings-article","created":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T23:32:39Z","timestamp":1707867159000},"page":"556-562","source":"Crossref","is-referenced-by-count":1,"title":["Parking Vehicle Shape Estimation Using Millimeter-Wave Radar Applying Semi-Supervised Learning"],"prefix":"10.1109","author":[{"given":"Tokihiko","family":"Akita","sequence":"first","affiliation":[{"name":"Toyota Technological Institute,Nagoya, Aichi,Japan,468-8511"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seiichi","family":"Mita","sequence":"additional","affiliation":[{"name":"Toyota Technological Institute,Nagoya, Aichi,Japan,468-8511"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Reconstruction of parking scene using high-resolution millimeter-wave radar","volume-title":"the 20th ITS Symposium 2022, 2-B-04","author":"Akita","year":"2022"},{"key":"ref2","first-page":"596","article-title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","volume-title":"NIPS\u201920: Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"Sohn","year":"2020"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8813808"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC45102.2020.9294210"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2016.2644615"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref7","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Workshop on challenges in representation learning","author":"Lee","year":"2013"},{"key":"ref8","first-page":"3365","article-title":"Learning with pseudo-ensembles","author":"Bachman","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ijcnn48605.2020.9207304"},{"key":"ref10","article-title":"Stochastic Reliability Estimation of Deep Learning for Parking Vehicle Shape Estimation Using Millimeter Wave Radar","volume-title":"Proceedings of JSAE Congress, ROMBUNNO","volume":"242","author":"Akita","year":"2022"},{"key":"ref11","first-page":"1","article-title":"Temporal Ensembling For Semi-Supervised Learning","author":"Laine","year":"2017","journal-title":"ICLR 2017"},{"key":"ref12","first-page":"1","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"31st Conference on Neural Information Processing Systems (NIPS 2017)","author":"Tarvainen","year":"2017"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref14","first-page":"5050","article-title":"Mixmatch: A holistic approach to semi-supervised learning","author":"Berthelot","year":"2019","journal-title":"Advances in Neural Information Processing Systems (NIPS)"},{"key":"ref15","article-title":"Remix-match: Semi-supervised learning with distribution matching and augmentation anchoring","volume-title":"Eighth International Conference on Learning Representations (ICLR)","author":"Berthelot","year":"2020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3084358"},{"key":"ref17","first-page":"1","volume-title":"Active Learning Literature Survey","author":"Settles","year":"2009"},{"key":"ref18","article-title":"Auto-Encoding Variational Bayes","volume-title":"International Conference on Learning Representations (ICLR)","author":"Kingma","year":"2014"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.20485\/jsaeijae.13.2_97"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2019.00607"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IV48863.2021.9575247"},{"key":"ref22","first-page":"25","article-title":"Recognition ability for millimeter-wave radar Hyper resolution by new principle at once, Romance of the Three Kingdoms in vehicular sensor field","author":"Amano","year":"2018","journal-title":"the 2nd division, Nikkei Electronics 2018.03"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.5244\/C.27.32"}],"event":{"name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","location":"Bilbao, Spain","start":{"date-parts":[[2023,9,24]]},"end":{"date-parts":[[2023,9,28]]}},"container-title":["2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10420842\/10420843\/10421807.pdf?arnumber=10421807","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T07:57:56Z","timestamp":1710403076000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10421807\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,24]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/itsc57777.2023.10421807","relation":{},"subject":[],"published":{"date-parts":[[2023,9,24]]}}}