{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T16:38:22Z","timestamp":1757608702854,"version":"3.44.0"},"reference-count":61,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"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":[[2025,5,19]]},"DOI":"10.1109\/icra55743.2025.11128376","type":"proceedings-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T17:28:56Z","timestamp":1756834136000},"page":"343-350","source":"Crossref","is-referenced-by-count":0,"title":["The Devil is in the Quality: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection"],"prefix":"10.1109","author":[{"given":"Zhipeng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Shanghai Jiao Tong University"}]},{"given":"Zhenyu","family":"Li","sequence":"additional","affiliation":[{"name":"King Abdullah University of Science and Technology"}]},{"given":"Hanshi","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA"}]},{"given":"Yuan","family":"He","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Shanghai Jiao Tong University"}]},{"given":"Ke","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Shanghai Jiao Tong University"}]},{"given":"Heng","family":"Fan","sequence":"additional","affiliation":[{"name":"University of North Texas,Department of CSE"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00469"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00330"},{"key":"ref3","article-title":"Depth map prediction from a single image using a multi-scale deep network","volume":"27","author":"Eigen","year":"2014","journal-title":"NeurIPS"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00506"},{"key":"ref5","article-title":"Monodetr: Depth-aware transformer for monocular 3d object detection","author":"Zhang","year":"2022","journal-title":"arXiv preprint"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00107"},{"key":"ref7","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume":"30","author":"Tarvainen","year":"2017","journal-title":"NeurIPS"},{"key":"ref8","article-title":"Mixmatch: A holistic approach to semi-supervised learning","volume":"32","author":"Berthelot","year":"2019","journal-title":"NeurIPS"},{"key":"ref9","first-page":"596","article-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"NeurIPS","author":"Sohn","year":"2020"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00305"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20077-9_3"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i3.20234"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611906"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20074-8_41"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01398"},{"key":"ref19","article-title":"Algorithms for hyper-parameter optimization","volume":"24","author":"Bergstra","year":"2011","journal-title":"NeurIPS"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2892405"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_9"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01169"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01489"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00310"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00398"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3276518"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3237579"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/iccvw.2019.00114"},{"key":"ref29","article-title":"Orthographic feature transform for monocular 3d object detection","author":"Roddick","year":"2018","journal-title":"arXiv preprint"},{"key":"ref30","article-title":"Objects as points","author":"Zhou","year":"2019","journal-title":"arXiv preprint"},{"key":"ref31","first-page":"1475","article-title":"Probabilistic and geometric depth: Detecting objects in perspective","author":"Wang","year":"2022","journal-title":"CoRL. PMLR"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"ref34","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford","year":"2015","journal-title":"arXiv preprint"},{"key":"ref35","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","author":"Lee","year":"2013","journal-title":"ICMLW"},{"key":"ref36","article-title":"Semi-supervised learning with ladder networks","volume":"28","author":"Rasmus","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3186041"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2020.3038663"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3270728"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1975.10479874"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1965.1053799"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2023.3301854"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128904"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20077-9_15"},{"key":"ref45","article-title":"Auto augment: Learning augmentation policies from data","author":"Cubuk","year":"2018","journal-title":"arXiv preprint"},{"key":"ref46","article-title":"Fast autoaugment","volume":"32","author":"Lim","year":"2019","journal-title":"NeurIPS"},{"key":"ref47","article-title":"Improved baselines with momentum contrastive learning","author":"Chen","year":"2020","journal-title":"arXiv preprint"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58601-0_19"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00052"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01024"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00845"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01535"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01211"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_38"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00164"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00115"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref59","article-title":"3d object proposals for accurate object class detection","volume":"28","author":"Chen","year":"2015","journal-title":"NeurIPS"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i2.20074"}],"event":{"name":"2025 IEEE International Conference on Robotics and Automation (ICRA)","start":{"date-parts":[[2025,5,19]]},"location":"Atlanta, GA, USA","end":{"date-parts":[[2025,5,23]]}},"container-title":["2025 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11127273\/11127223\/11128376.pdf?arnumber=11128376","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T06:46:28Z","timestamp":1756881988000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11128376\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,19]]},"references-count":61,"URL":"https:\/\/doi.org\/10.1109\/icra55743.2025.11128376","relation":{},"subject":[],"published":{"date-parts":[[2025,5,19]]}}}