{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:45:11Z","timestamp":1761061511664,"version":"3.37.3"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Government Grant managed"},{"name":"French National Research Agency through the Future Investment Program","award":["ANR-19-STHP-0006"],"award-info":[{"award-number":["ANR-19-STHP-0006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3124678","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T20:24:18Z","timestamp":1635798258000},"page":"149780-149795","source":"Crossref","is-referenced-by-count":7,"title":["Improving Unsupervised Domain Adaptive Re-Identification Via Source-Guided Selection of Pseudo-Labeling Hyperparameters"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7171-708X","authenticated-orcid":false,"given":"Fabian","family":"Dubourvieux","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5427-3010","authenticated-orcid":false,"given":"Angelique","family":"Loesch","sequence":"additional","affiliation":[]},{"given":"Romaric","family":"Audigier","sequence":"additional","affiliation":[]},{"given":"Samia","family":"Ainouz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7602-4557","authenticated-orcid":false,"given":"Stephane","family":"Canu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_6"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00271"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/127"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_10"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00205"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58555-6_14"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICMEW.2019.00084"},{"key":"ref36","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00382"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00321"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00904"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00344"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_35"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00831"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_53"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_31"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413904"},{"key":"ref65","first-page":"3538","article-title":"Exploiting sample uncertainty for domain adaptive person re-identification","author":"zheng","year":"2021","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00527"},{"key":"ref29","first-page":"1","article-title":"Conditional adversarial domain adaptation","author":"long","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.133"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_11"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347176"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00069"},{"journal-title":"GPyOpt A bayesian optimization framework in python","year":"2016","key":"ref1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_43"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00801"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00054"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018738"},{"key":"ref23","first-page":"1","article-title":"Multi-task mid-level feature alignment network for unsupervised cross-dataset person re-identification","author":"lin","year":"2018","journal-title":"Proc 29th Brit Mach Vis Conf (BMVC)"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.238"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2982826"},{"key":"ref50","first-page":"22","author":"vapnik","year":"1998","journal-title":"Statistical Learning Theory"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01099"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01367"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00225"},{"key":"ref57","first-page":"7124","article-title":"Towards accurate model selection in deep unsupervised domain adaptation","author":"you","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3054775"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00482"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6950"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01175"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00016"},{"key":"ref10","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","author":"ester","year":"1996","journal-title":"Proc KDD"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3056212"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00835"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00621"},{"key":"ref13","first-page":"2030","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2015","journal-title":"J Mach Learn Res"},{"key":"ref14","first-page":"1","article-title":"Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification","author":"ge","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref15","first-page":"11309","article-title":"Self-paced contrastive learning with hybrid memory for domain adaptive object re-ID","author":"ge","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref17","first-page":"1989","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","author":"hoffman","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/BF01908075"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58604-1_2"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_38"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013288"},{"key":"ref6","first-page":"442","article-title":"Learning bounds for importance weighting","author":"cortes","year":"2010","journal-title":"Proc NIPS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00204"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00110"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412964"},{"key":"ref46","article-title":"Covariate shift adaptation by importance weighted cross validation","volume":"8","author":"sugiyama","year":"2007","journal-title":"J Mach Learn Res"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107173"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00195"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00070"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.2307\/2284239"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00817"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref43","first-page":"17","article-title":"Performance measures and a data set for multi-target, multi-camera tracking","author":"ristani","year":"2016","journal-title":"Proc Eur Conf Comput Vis"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09597556.pdf?arnumber=9597556","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T21:05:34Z","timestamp":1641848734000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9597556\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":71,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3124678","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}