{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T05:51:06Z","timestamp":1780379466458,"version":"3.54.1"},"reference-count":109,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2019YFB2101900"],"award-info":[{"award-number":["2019YFB2101900"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976151"],"award-info":[{"award-number":["61976151"]}],"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":["61925602"],"award-info":[{"award-number":["61925602"]}],"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":["61732011"],"award-info":[{"award-number":["61732011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"A*STAR AI3 HTPO Seed Fund","award":["C211118012"],"award-info":[{"award-number":["C211118012"]}]},{"name":"Joey Tianyi Zhou's A*STAR SERCCentral Research Fund"},{"name":"AME Programmatic Funding Scheme","award":["A18A1b0045"],"award-info":[{"award-number":["A18A1b0045"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,2,1]]},"DOI":"10.1109\/tpami.2022.3171983","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T20:16:28Z","timestamp":1651608988000},"page":"2551-2566","source":"Crossref","is-referenced-by-count":393,"title":["Trusted Multi-View Classification With Dynamic Evidential Fusion"],"prefix":"10.1109","volume":"45","author":[{"given":"Zongbo","family":"Han","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1410-6650","authenticated-orcid":false,"given":"Changqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9702-5524","authenticated-orcid":false,"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4675-7055","authenticated-orcid":false,"given":"Joey Tianyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"A*STAR Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475379"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01102"},{"key":"ref3","first-page":"559","article-title":"CPM-Nets: Cross partial multi-view networks","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Zhang","year":"2019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.2296528"},{"key":"ref5","first-page":"6850","article-title":"One pass late fusion multi-view clustering","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu","year":"2021"},{"key":"ref6","first-page":"5092","article-title":"Comic: Multi-view clustering without parameter selection","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Peng","year":"2019"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2651379"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"ref9","article-title":"End to end learning for self-driving cars","author":"Bojarski","year":"2016"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2002.1038076"},{"key":"ref11","first-page":"568","article-title":"Two-stream convolutional networks for action recognition in videos","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Simonyan","year":"2014"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1656"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247814"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00713"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/1027527.1027661"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-010-0182-0"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1303"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/nature08538"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-05432-w"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2019.08.038"},{"key":"ref22","first-page":"1356","article-title":"Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts","volume":"33","author":"Charpentier","year":"2020","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref23","article-title":"Bayesian methods for adaptive models","author":"MacKay","year":"1992"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316870"},{"key":"ref25","volume-title":"Bayesian Learning for Neural Networks","volume":"118","author":"Neal","year":"2012"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.3.448"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.5555\/2986459.2986721"},{"key":"ref28","first-page":"814","article-title":"Black box variational inference","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Ranganath","year":"2014"},{"key":"ref29","first-page":"1613","article-title":"Weight uncertainty in neural network","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Blundell","year":"2015"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045502"},{"issue":"1","key":"ref31","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref32","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lakshminarayanan","year":"2017"},{"key":"ref33","first-page":"3179","article-title":"Evidential deep learning to quantify classification uncertainty","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sensoy","year":"2018"},{"key":"ref34","first-page":"9690","article-title":"Uncertainty estimation using a single deep deterministic neural network","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Amersfoort","year":"2020"},{"key":"ref35","first-page":"1","article-title":"Trusted multi-view classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Han","year":"2021"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_14"},{"key":"ref37","article-title":"A Kernel method for canonical correlation analysis","author":"Akaho","year":"2006"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2007.891186"},{"key":"ref39","first-page":"1247","article-title":"Deep canonical correlation analysis","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Andrew","year":"2013"},{"key":"ref40","first-page":"1083","article-title":"On deep multi-view representation learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wang","year":"2015"},{"key":"ref41","article-title":"Deep variational canonical correlation analysis","author":"Wang","year":"2016"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.312"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"ref44","first-page":"15535","article-title":"Learning representations by maximizing mutual information across views","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bachman","year":"2019"},{"key":"ref45","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen","year":"2020"},{"key":"ref46","first-page":"4116","article-title":"Contrastive multi-view representation learning on graphs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hassani","year":"2020"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11945"},{"key":"ref48","article-title":"Revisiting the effectiveness of off-the-shelf temporal modeling approaches for large-scale video classification","author":"Bian","year":"2017"},{"key":"ref49","article-title":"Supervised multimodal bitransformers for classifying images and text","author":"Kiela","year":"2019"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01271"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/524"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2879108"},{"key":"ref53","article-title":"AMVNet: Assertion-based multi-view fusion network for lidar semantic segmentation","author":"Liong","year":"2020"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44415-3_16"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2003.817150"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2013.2267205"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3117403"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3136094"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2019.2899222"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-23774-w"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00631"},{"key":"ref62","article-title":"Cooperative learning for multi-view analysis","author":"Ding","year":"2021"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00640"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9197266"},{"key":"ref65","first-page":"853","article-title":"Transforming neural-net output levels to probability distributions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Denker","year":"1991"},{"key":"ref66","first-page":"1","article-title":"Training independent subnetworks for robust prediction","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Havasi","year":"2021"},{"key":"ref67","article-title":"Depth uncertainty in neural networks","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Antor\u00e1n","year":"2020"},{"key":"ref68","article-title":"Predictive uncertainty estimation via prior networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Malinin","year":"2018"},{"key":"ref69","article-title":"Ensemble distribution distillation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Malinin","year":"2020"},{"key":"ref70","first-page":"5707","article-title":"Evaluating robustness of predictive uncertainty estimation: Are dirichlet-based models reliable?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kopetzki","year":"2021"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177698950"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1968.tb00722.x"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1515\/9780691214696"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.2172\/800792"},{"key":"ref75","first-page":"1225","article-title":"Interpretation and fusion of hyper opinions in subjective logic","volume-title":"Proc. 15th Int. Conf. Inf. Fusion","author":"J\u00f8sang","year":"2012"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2017.2659764"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8655(96)00039-6"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/MFI.2010.5604480"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2005.07.003"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/36.602544"},{"key":"ref81","first-page":"7034","article-title":"Confidence-aware learning for deep neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Moon","year":"2020"},{"key":"ref82","first-page":"1","article-title":"Predictive uncertainty estimation via prior networks","volume":"31","author":"Malinin","year":"2018","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref83","first-page":"1356","article-title":"Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Charpentier","year":"2020"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-45528-0"},{"key":"ref85","first-page":"1","article-title":"Auto-encoding variational bayes","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma","year":"2014"},{"key":"ref86","first-page":"1","article-title":"Beta-VAE: Learning basic visual concepts with a constrained variational framework","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Higgins","year":"2017"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-42337-1"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01310"},{"key":"ref89","first-page":"1","article-title":"Introduction to the Dirichlet distribution and related processes","author":"Frigyik","year":"2010"},{"key":"ref90","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guo","year":"2017"},{"key":"ref91","first-page":"1","volume-title":"The Caltech-UCSD Birds-200\u20132011 Dataset","author":"Wah","year":"2011"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2004.383"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.16"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"ref95","first-page":"909","article-title":"Uncertainty-aware attention for reliable interpretation and prediction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Heo","year":"2018"},{"key":"ref96","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.645"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2018.8486573"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12292"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2872629"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019176"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01211"},{"key":"ref103","first-page":"1","article-title":"Centroid based concept learning for RGB-D indoor scene classification","volume-title":"Proc. Brit. Mach. Vis. Conf.","author":"Ayub","year":"2020"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/631"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298655"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/ICMEW.2015.7169757"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10008914\/09767662.pdf?arnumber=9767662","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T21:51:50Z","timestamp":1705960310000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9767662\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,1]]},"references-count":109,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3171983","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":[[2023,2,1]]}}}