{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T14:51:12Z","timestamp":1770994272095,"version":"3.50.1"},"reference-count":54,"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\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) Support Program supervised by the Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2020-2020-0-01789"],"award-info":[{"award-number":["IITP-2020-2020-0-01789"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF) funded by the MSIT through the Basic Science Research Program","doi-asserted-by":"publisher","award":["NRF-2020R1A2C1006179"],"award-info":[{"award-number":["NRF-2020R1A2C1006179"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF) funded by the MSIT through the Basic Science Research Program","doi-asserted-by":"publisher","award":["NRF-2019R1A2C1083813"],"award-info":[{"award-number":["NRF-2019R1A2C1083813"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3051637","type":"journal-article","created":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T21:52:02Z","timestamp":1610661122000},"page":"12055-12066","source":"Crossref","is-referenced-by-count":8,"title":["AS-RIG: Adaptive Selection of Reconstructed Input by Generator or Interpolation for Person Re-Identification in Cross-Modality Visible and Thermal Images"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9293-4502","authenticated-orcid":false,"given":"Jin Kyu","family":"Kang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0661-6888","authenticated-orcid":false,"given":"Min Beom","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2917-4613","authenticated-orcid":false,"given":"Hyo Sik","family":"Yoon","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1214-9510","authenticated-orcid":false,"given":"Kang Ryoung","family":"Park","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","year":"2020","journal-title":"Geforce GTX 1070"},{"key":"ref38","first-page":"26","article-title":"Lecture 6.5–‘RMSProp: Divide the gradient by a running average of its recent magnitude","author":"tieleman","year":"2012"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref32","author":"salomon","year":"2006","journal-title":"Data Compression The Complete Reference"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01027"},{"key":"ref37","first-page":"2121","article-title":"Adaptive subgradient method for online learning and stochastic optimization","volume":"12","author":"durchi","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref36","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref35","year":"2020","journal-title":"Thermal Imaging Cameras"},{"key":"ref34","year":"2020","journal-title":"C600 Webcam Camera"},{"key":"ref28","first-page":"1222","article-title":"FD-GAN: Pose-guided feature distilling GAN for robust person re-identification","author":"ge","year":"2018","journal-title":"Proc Adv Nueral Inf Process Syst"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00016"},{"key":"ref29","article-title":"RGB-IR cross-modality person ReID based on teacher-student GAN model","author":"zhang","year":"2020","journal-title":"arXiv 2007 07452"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.079"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2012.07.005"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/152"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2914670"},{"key":"ref21","first-page":"7501","article-title":"Hierarchical discriminative learning for visible thermal person re-identification","author":"ye","year":"2018","journal-title":"Proc Assoc Advancement Artif Intell Conf Artif Intell"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref23","article-title":"HPILN: A feature learning framework for cross-modality person re-identification","author":"lin","year":"2019","journal-title":"arXiv 1906 03142"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00541"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2651364"},{"key":"ref51","first-page":"229","article-title":"Dynamic dual-attentive aggregation learning for visible-infrared person re-identification","author":"ye","year":"2020","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3013379"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2921454"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2998275"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01290-1"},{"key":"ref11","first-page":"1276","article-title":"Object identification: A Bayesian context","author":"huang","year":"1997","journal-title":"Proc 15th Int Joint Conf Artif Intell"},{"key":"ref40","year":"2020","journal-title":"MAP"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539926"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299016"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00902"},{"key":"ref15","first-page":"13","article-title":"A discriminatively learned CNN embedding for person re-identification","volume":"14","author":"zheng","year":"2017","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.149"},{"key":"ref17","article-title":"In defense of the triplet loss for person re-identification","author":"hermans","year":"2017","journal-title":"arXiv 1703 07737"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.145"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2675201"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1049\/trit.2018.1001"},{"key":"ref3","article-title":"Person re-identification: Past, present and future","author":"zheng","year":"2016","journal-title":"arXiv 1610 02984"},{"key":"ref6","article-title":"Deep learning for person re-identification: A survey and outlook","author":"ye","year":"2020","journal-title":"arXiv 2001 04193"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2957336"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3001665"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00372"},{"key":"ref49","first-page":"2669","article-title":"Scale-adaptive low-resolution person re-identification via learning a discriminating surface","author":"wang","year":"2016","journal-title":"Proc Int Joint Conf Artif Intell"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/s17030605"},{"key":"ref46","year":"2020","journal-title":"Jetson TX2 System"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2948260"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.429"},{"key":"ref47","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref42","year":"2020","journal-title":"PyTorch"},{"key":"ref41","year":"2020","journal-title":"AS-RIG With Algorithm"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2944970"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3390\/rs9080790"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09324824.pdf?arnumber=9324824","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:12Z","timestamp":1639770972000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9324824\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":54,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3051637","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}