{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:03:27Z","timestamp":1765357407582,"version":"3.37.3"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["2021-0-00951,2022R1A2C200494412"],"award-info":[{"award-number":["2021-0-00951,2022R1A2C200494412"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,29]]},"DOI":"10.1109\/icra48891.2023.10160433","type":"proceedings-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T17:20:56Z","timestamp":1688491256000},"page":"4819-4825","source":"Crossref","is-referenced-by-count":4,"title":["Joint Semi-Supervised and Active Learning via 3D Consistency for 3D Object Detection"],"prefix":"10.1109","author":[{"given":"Sihwan","family":"Hwang","sequence":"first","affiliation":[{"name":"Cho Chun Shik Graduate School of Mobility, KAIST,Daejeon,Republic of Korea,34051"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanmin","family":"Kim","sequence":"additional","affiliation":[{"name":"Cho Chun Shik Graduate School of Mobility, KAIST,Daejeon,Republic of Korea,34051"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youngseok","family":"Kim","sequence":"additional","affiliation":[{"name":"Cho Chun Shik Graduate School of Mobility, KAIST,Daejeon,Republic of Korea,34051"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongsuk","family":"Kum","sequence":"additional","affiliation":[{"name":"Cho Chun Shik Graduate School of Mobility, KAIST,Daejeon,Republic of Korea,34051"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20876-9_32"},{"key":"ref35","article-title":"Auto-encoding variational bayes","author":"kingma","year":"2013","journal-title":"ArXiv Preprint"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00018"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/584091.584093"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304793"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref14","article-title":"Deep active learning for object detection","volume":"91","author":"roy","year":"0","journal-title":"BMVC"},{"key":"ref36","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3390\/s18103337"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01161"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00377"},{"journal-title":"Mixture density networks","year":"1994","author":"bishop","key":"ref33"},{"key":"ref10","article-title":"Active learning for deep object detection","author":"brust","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref32","article-title":"3d object proposals for accurate object class detection","volume":"28","author":"chen","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref2","article-title":"Bayesian active learning for classification and preference learning","author":"houlsby","year":"2011","journal-title":"ArXiv Preprint"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00976"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00529"},{"key":"ref39","first-page":"1558","article-title":"Autoencoding beyond pixels using a learned similarity metric","author":"larsen","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01010"},{"key":"ref38","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier gans","author":"odena","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref19","article-title":"Combining active learning and semi-supervised learning using gaussian fields and harmonic functions","volume":"3","author":"zhu","year":"0","journal-title":"ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_30"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304565"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8814236"},{"key":"ref26","article-title":"Consistency-based semi-supervised learning for object detection","volume":"32","author":"jeong","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref25","article-title":"Just label what you need: fine-grained active selection for perception and prediction through partially labeled scenes","author":"segal","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-12637-1_27"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref41","first-page":"740","article-title":"Microsoft coco: Common objects in context","author":"lin","year":"0","journal-title":"European Conference on Computer Vision"},{"key":"ref22","article-title":"Importance of self-consistency in active learning for semantic segmentation","author":"golestaneh","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref44","first-page":"2446","article-title":"Others Scalability in perception for autonomous driving: Waymo open dataset","author":"sun","year":"0","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01409"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01426"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01109"},{"key":"ref29","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume":"30","author":"tarvainen","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref8","article-title":"Active learning for convolutional neural networks: A core-set approach","author":"sener","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00607"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_9"},{"key":"ref4","article-title":"Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning","volume":"32","author":"kirsch","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref3","first-page":"1183","article-title":"Deep bayesian active learning with image data","author":"gal","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref6","first-page":"6295","article-title":"Bayesian generative active deep learning","author":"tran","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref5","article-title":"Generative adversarial active learning","author":"zhu","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"}],"event":{"name":"2023 IEEE International Conference on Robotics and Automation (ICRA)","start":{"date-parts":[[2023,5,29]]},"location":"London, United Kingdom","end":{"date-parts":[[2023,6,2]]}},"container-title":["2023 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10160211\/10160212\/10160433.pdf?arnumber=10160433","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T17:35:40Z","timestamp":1690220140000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10160433\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,29]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/icra48891.2023.10160433","relation":{},"subject":[],"published":{"date-parts":[[2023,5,29]]}}}