{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T03:45:02Z","timestamp":1774583102266,"version":"3.50.1"},"reference-count":78,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022ZD0160104"],"award-info":[{"award-number":["2022ZD0160104"]}]},{"name":"National Key R&amp;D Program of China","award":["NSFC"],"award-info":[{"award-number":["NSFC"]}]},{"name":"National Key R&amp;D Program of China","award":["62206172"],"award-info":[{"award-number":["62206172"]}]},{"name":"Shanghai Committee of Science and Technology","award":["23YF1462000"],"award-info":[{"award-number":["23YF1462000"]}]},{"name":"JC STEM Lab of Autonomous Intelligent Systems"},{"name":"The Hong Kong Jockey Club Charities Trust"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tpami.2025.3642076","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T18:32:53Z","timestamp":1765305173000},"page":"3666-3679","source":"Crossref","is-referenced-by-count":1,"title":["Test-Time Correction: An Online 3D Detection System via Visual Prompting"],"prefix":"10.1109","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2182-1932","authenticated-orcid":false,"given":"Hanxue","family":"Zhang","sequence":"first","affiliation":[{"name":"Shanghai AI Lab, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zetong","family":"Yang","sequence":"additional","affiliation":[{"name":"GAC R&amp;D Center, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1369-2902","authenticated-orcid":false,"given":"Yanan","family":"Sun","sequence":"additional","affiliation":[{"name":"Shanghai AI Lab, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong, SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2614-5369","authenticated-orcid":false,"given":"Fei","family":"Xia","sequence":"additional","affiliation":[{"name":"Meituan Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatma","family":"G\u00fcney","sequence":"additional","affiliation":[{"name":"Ko&#x00E7; University, Istanbul, T&#x00FC;rkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9110-5534","authenticated-orcid":false,"given":"Hongyang","family":"Li","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong, SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01712"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19839-7_31"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01417"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01580"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20077-9_1"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01710"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19812-0_31"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00845"},{"key":"ref9","article-title":"BEVDet: High-performance multi-camera 3D object detection in bird-eye-view","author":"Huang","year":"2021"},{"key":"ref10","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Wei","year":"2022"},{"key":"ref11","article-title":"Kosmos-2: Grounding multimodal large language models to the world","author":"Peng","year":"2023"},{"key":"ref12","article-title":"GPT-4 technical report","year":"2023"},{"key":"ref13","article-title":"Gemini: A family of highly capable multimodal models","author":"Team","year":"2023"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02496"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i2.27897"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-023-3943-6"},{"key":"ref17","article-title":"Sam2point: Segment any 3D as videos in zero-shot and promptable manners","author":"Guo","year":"2024"},{"key":"ref18","first-page":"71862","article-title":"CoDA: Collaborative novel box discovery and cross-modal alignment for open-vocabulary 3D object detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cao","year":"2024"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/iccv51070.2023.00840"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00351"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01298"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01161"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_12"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72784-9_11"},{"key":"ref27","article-title":"OmniDrive: A holistic LLM-agent framework for autonomous driving with 3D perception, reasoning and planning","author":"Wang","year":"2024"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01157"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-25056-9_43"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ISMAR.2008.4637336"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00500"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3168781"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01028"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01400"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01324"},{"key":"ref36","first-page":"58736","article-title":"MixFormerV2: Efficient fully transformer tracking","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cui","year":"2024"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015719"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00136"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02037"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.47"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.233"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref43","article-title":"Anything-3D: Towards single-view anything reconstruction in the wild","author":"Shen","year":"2023"},{"key":"ref44","article-title":"Grounded SAM: Assembling open-world models for diverse visual tasks","author":"Ren","year":"2024"},{"key":"ref45","article-title":"Track anything: Segment anything meets videos","author":"Yang","year":"2023"},{"key":"ref46","first-page":"19769","article-title":"Segment everything everywhere all at once","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zou","year":"2023"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612680"},{"key":"ref48","first-page":"41647","article-title":"AdaNPC: Exploring non-parametric classifier for test-time adaptation","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Zhang","year":"2023"},{"key":"ref49","first-page":"16888","article-title":"Efficient test-time model adaptation without forgetting","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Niu","year":"2022"},{"key":"ref50","first-page":"16888","article-title":"Towards stable test-time adaptation in dynamic wild world","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Niu","year":"2023"},{"key":"ref51","first-page":"11539","article-title":"Improving robustness against common corruptions by covariate shift adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Schneider","year":"2020"},{"key":"ref52","first-page":"1","article-title":"Tent: Fully test-time adaptation by entropy minimization","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Wang","year":"2021"},{"key":"ref53","first-page":"21808","article-title":"TTT++: When does self-supervised test-time training fail or thrive?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu","year":"2021"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00110"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00055"},{"key":"ref56","article-title":"Exploring test-time adaptation for object detection in continually changing environments","author":"Cao","year":"2024"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72784-9_6"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72775-7_12"},{"key":"ref59","first-page":"1","article-title":"MOS: Model synergy for test-time adaptation on LiDAR-based 3D object detection","volume-title":"Proc. 13th Int. Conf. Representations","author":"Chen","year":"2024"},{"key":"ref60","first-page":"180","article-title":"DETR3D: 3D object detection from multi-view images via 3D-to-2D queries","volume-title":"Proc. Conf. Robot Learn.","author":"Wang","year":"2021"},{"key":"ref61","first-page":"7537","article-title":"Fourier features let networks learn high frequency functions in low dimensional domains","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tancik","year":"2020"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72967-6_12"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00335"},{"key":"ref69","article-title":"Sparse4D v2: Recurrent temporal fusion with sparse model","author":"Lin","year":"2023"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3227513"},{"key":"ref71","article-title":"MMDetection3D: OpenMMLab next-generation platform for general 3D object detection","author":"Contributors","year":"2020"},{"key":"ref72","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 13th Int. Conf. Representations","author":"Kingma","year":"2015"},{"key":"ref73","first-page":"1","article-title":"Decoupled weight decay regularization","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Loshchilov","year":"2019"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72970-6_3"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00479"},{"key":"ref76","first-page":"20413","article-title":"An embodied generalist agent in 3D world","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Huang","year":"2024"},{"key":"ref77","first-page":"1903","article-title":"Cross-dataset sensor alignment: Making visual 3D object detector generalizable","volume-title":"Proc. Conf. Robot Learn.","author":"Zheng","year":"2023"},{"key":"ref78","article-title":"Ultralytics YOLOv8","author":"Jocher","year":"2023"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11372200\/11288026.pdf?arnumber=11288026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:05:51Z","timestamp":1770671151000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11288026\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":78,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3642076","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]}}}