{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T17:56:56Z","timestamp":1774375016530,"version":"3.50.1"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100020487","name":"Nature","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100020487","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020487","name":"Nature","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100020487","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,30]]},"DOI":"10.1109\/icra48506.2021.9561757","type":"proceedings-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:28:35Z","timestamp":1634689715000},"page":"14213-14219","source":"Crossref","is-referenced-by-count":11,"title":["Exploiting Probabilistic Siamese Visual Tracking with a Conditional Variational Autoencoder"],"prefix":"10.1109","author":[{"given":"Wenhui","family":"Huang","sequence":"first","affiliation":[{"name":"Shandong Normal University,School of Information Science and Engineering,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Gu","sequence":"additional","affiliation":[{"name":"Dalhousie University,Department of Electrical and Computer Engineering,Halifax,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiyong","family":"Duan","sequence":"additional","affiliation":[{"name":"Shandong Normal University,School of Information Science and Engineering,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sujuan","family":"Hou","sequence":"additional","affiliation":[{"name":"Shandong Normal University,School of Information Science and Engineering,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjie","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shandong Normal University,School of Information Science and Engineering,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2345390"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00140"},{"key":"ref32","article-title":"The sixth visual object tracking vot2018 challenge results","author":"kristan","year":"2018","journal-title":"ECCV workshop"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00508"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00804"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.65"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.531"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.468"},{"key":"ref34","first-page":"850","article-title":"Fully-convolutional siamese networks for object tracking","author":"bertinetto","year":"2016","journal-title":"ECCV workshop"},{"key":"ref10","first-page":"7482","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","author":"kendall","year":"2018","journal-title":"CVPR"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00937-3_1"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00721"},{"key":"ref13","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"ICML"},{"key":"ref14","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"lakshminarayanan","year":"2017","journal-title":"NIPS"},{"key":"ref15","first-page":"318","article-title":"Evaluating scalable bayesian deep learning methods for robust computer vision","author":"gustafsson","year":"2020","journal-title":"CVPR Workshops"},{"key":"ref16","first-page":"13991","article-title":"Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift","author":"ovadia","year":"2019","journal-title":"NIPS"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01605-2"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00142"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00661"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2759583"},{"key":"ref27","first-page":"4847","article-title":"Discriminative correlation filter with channel and spatial reliability","author":"lukezic","year":"2017","journal-title":"CVPR"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2316\/Journal.206.2016.5.206-4729"},{"key":"ref6","first-page":"3483","article-title":"Learning structured output representation using deep conditional generative models","author":"sohn","year":"2015","journal-title":"NIPS"},{"key":"ref29","article-title":"Modeling and propagating cnns in a tree structure for visual tracking","author":"nam","year":"2016","journal-title":"CoRR"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.5244\/C.31.57"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00575"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00700"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.26.1.013016"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00300"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989513"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00716"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00676"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00479"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00376"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00935"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00480"}],"event":{"name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","location":"Xi'an, China","start":{"date-parts":[[2021,5,30]]},"end":{"date-parts":[[2021,6,5]]}},"container-title":["2021 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9560720\/9560666\/09561757.pdf?arnumber=9561757","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T23:21:28Z","timestamp":1659482488000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9561757\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,30]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/icra48506.2021.9561757","relation":{},"subject":[],"published":{"date-parts":[[2021,5,30]]}}}