{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T23:43:17Z","timestamp":1778802197136,"version":"3.51.4"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFB4501600"],"award-info":[{"award-number":["2022YFB4501600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFB4501603"],"award-info":[{"award-number":["2022YFB4501603"]}],"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":["62102383"],"award-info":[{"award-number":["62102383"]}],"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":["61976200"],"award-info":[{"award-number":["61976200"]}],"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":["62172380"],"award-info":[{"award-number":["62172380"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Jiangsu Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["BK20241818"],"award-info":[{"award-number":["BK20241818"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"publisher","award":["Y2021121"],"award-info":[{"award-number":["Y2021121"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"name":"USTC Research Funds of the Double First-Class Initiative","award":["YD2150002011"],"award-info":[{"award-number":["YD2150002011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1109\/tai.2024.3474654","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T13:54:51Z","timestamp":1728309291000},"page":"221-233","source":"Crossref","is-referenced-by-count":3,"title":["Knowledge Probabilization in Ensemble Distillation: Improving Accuracy and Uncertainty Quantification for Object Detectors"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1063-7182","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9403-5575","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8391-5526","authenticated-orcid":false,"given":"Lei","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-3600","authenticated-orcid":false,"given":"Min","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R) and Centre for Frontier AI Research (CFAR), A*STAR, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1719-0328","authenticated-orcid":false,"given":"Zhenghua","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R) and Centre for Frontier AI Research (CFAR), A*STAR, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0142-2483","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Data Science and Engineering, East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianglan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8360-3143","authenticated-orcid":false,"given":"Xuehai","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.312"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCS54944.2021.00021"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1017\/S1472669619000021"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/qj.456"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.2307\/2287720"},{"issue":"3","key":"ref6","first-page":"61","article-title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","volume":"10","author":"Platt","year":"1999","journal-title":"Adv. Large Margin Classifiers"},{"key":"ref7","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guo","year":"2017"},{"key":"ref8","first-page":"38706","article-title":"Towards improving calibration in object detection under domain shift","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Munir","year":"2022"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3241587"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108498"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3287137"},{"key":"ref12","volume-title":"Gaussian Processes for Machine Learning","volume":"2","author":"Williams","year":"2006"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00059"},{"key":"ref14","first-page":"6393","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lakshminarayanan","year":"2017"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107096"},{"key":"ref16","first-page":"2805","article-title":"Trainable calibration measures for neural networks from kernel mean embeddings","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kumar","year":"2018"},{"key":"ref17","article-title":"Improved trainable calibration method for neural networks on medical imaging classification","author":"Liang","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00999"},{"key":"ref19","first-page":"16468","article-title":"Instance-conditional knowledge distillation for object detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Kang","year":"2021"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00507"},{"key":"ref21","first-page":"5213","article-title":"Distilling object detectors with feature richness","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Zhang","year":"2021"},{"key":"ref22","first-page":"8367","article-title":"Diversity matters when learning from ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Nam","year":"2021"},{"key":"ref23","first-page":"11117","article-title":"Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang","year":"2020"},{"key":"ref24","first-page":"5567","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Kendall","year":"2017"},{"key":"ref25","article-title":"Predictive uncertainty estimation via prior networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Malinin","year":"2018"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17124"},{"key":"ref27","first-page":"17390","article-title":"Blurs behave like ensembles: Spatial smoothings to improve accuracy, uncertainty, and robustness","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Park","year":"2022"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref30","first-page":"83","article-title":"Faster r-CNN: Towards real-time object detection with region proposal networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Ren","year":"2015"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref32","first-page":"21002","article-title":"Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Li","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01320"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00919"},{"key":"ref35","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015"},{"key":"ref36","first-page":"734","article-title":"Learning efficient object detection models with knowledge distillation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Chen","year":"2017"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.776"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.383267"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.272"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/1180639.1180824"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-018-1072-8"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref48","article-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00171"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00894"},{"key":"ref51","article-title":"MMDetection: Open mmlab detection toolbox benchmark","author":"Chen","year":"2019"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00775"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00460"},{"key":"ref54","first-page":"16353","article-title":"Improving ensemble distillation with weight averaging and diversifying perturbation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Nam","year":"2022"},{"key":"ref55","first-page":"609","article-title":"Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"1","author":"Zadrozny","year":"2001"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9602"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3096854"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00219"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9078688\/10841832\/10706587.pdf?arnumber=10706587","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T19:01:01Z","timestamp":1764270061000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10706587\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":58,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tai.2024.3474654","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}