{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:27:08Z","timestamp":1740122828450,"version":"3.37.3"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61872030"],"award-info":[{"award-number":["61872030"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Major Science and Technology Innovation Project of Shandong Province","award":["2019TSLH0206"],"award-info":[{"award-number":["2019TSLH0206"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s11042-021-10589-6","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T17:51:06Z","timestamp":1612288266000},"page":"15215-15232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Unsupervised domain adaptive person re-identification via camera penalty learning"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1322-7269","authenticated-orcid":false,"given":"Xiaodi","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Houjin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"10589_CR1","doi-asserted-by":"crossref","unstructured":"Bolle R M, Connell J H, Pankanti S, et al(2005) The relation between the ROC curve and the CMC.Fourth IEEE workshop on automatic identification advanced technologies (AutoID'05) 15-20","DOI":"10.1109\/AUTOID.2005.48"},{"key":"10589_CR2","doi-asserted-by":"crossref","unstructured":"Chen Y, Zhu X, Gong S (2019) Instance-guided context rendering for cross-domain person re-identification. In: 2019 proceedings of the IEEE international conference on computer vision (ECCV) 232-242","DOI":"10.1109\/ICCV.2019.00032"},{"issue":"3","key":"10589_CR3","doi-asserted-by":"publisher","first-page":"3533","DOI":"10.1007\/s11042-017-5182-z","volume":"77","author":"D Cheng","year":"2017","unstructured":"Cheng D, Gong Y, Shi W, Zhang S (2017) Person re-identification by the asymmetric triplet and identification loss function. Multimed Tools Appl 77(3):3533\u20133550","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"10589_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3243316","volume":"14","author":"H Fan","year":"2018","unstructured":"Fan H, Zheng L, Yan C, Yang Y (2018) Unsupervised person re-identification: clustering and fine-tuning. ACM Trans Multimed Comput Commun Appl 14(4):1\u201318","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"10589_CR5","doi-asserted-by":"crossref","unstructured":"Fu Y, Wei Y, Wang G, Zhou Y, Shi H, Huang T-S (2019) Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification. In: 2019 proceedings of the IEEE international conference on computer vision (ECCV) 6112\u20136121","DOI":"10.1109\/ICCV.2019.00621"},{"key":"10589_CR6","unstructured":"Ge Y-X, Zhu F, Zhao R, Li H-S (2020) Structured domain adaptation for unsupervised person re-identification. arXiv:200306650"},{"key":"10589_CR7","unstructured":"Ge Y-X, Chen D-P, Li H-S (2020) Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification. In: 2020 international conference on learning representations (ICLR)."},{"issue":"5","key":"10589_CR8","doi-asserted-by":"publisher","first-page":"5843","DOI":"10.1007\/s11042-018-6409-3","volume":"78","author":"A Gen\u00e7","year":"2018","unstructured":"Gen\u00e7 A, Ekenel HK (2018) Cross-dataset person re-identification using deep convolutional neural networks: effects of context and domain adaptation. Multimed Tools Appl 78(5):5843\u20135861","journal-title":"Multimed Tools Appl"},{"key":"10589_CR9","unstructured":"Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification. arXiv:170307737"},{"key":"10589_CR10","doi-asserted-by":"crossref","unstructured":"Li W, Zhao R, Xiao T, Wang X (2014) DeepReID: deep filter pairing neural network for person re-identification. In: 2014 IEEE conference on computer vision and pattern recognition (CVPR) 152-159","DOI":"10.1109\/CVPR.2014.27"},{"key":"10589_CR11","doi-asserted-by":"crossref","unstructured":"Li K, Ding Z, Li K, Zhang Y, Fu Y (2018) Support neighbor loss for person re-identification. ACM Trans Multimed Comput Commun Appl 1492\u20131500","DOI":"10.1145\/3240508.3240674"},{"issue":"7","key":"10589_CR12","doi-asserted-by":"publisher","first-page":"1770","DOI":"10.1109\/TPAMI.2019.2903058","volume":"42","author":"M Li","year":"2020","unstructured":"Li M, Zhu X, Gong S (2020) Unsupervised Tracklet person re-identification. IEEE Trans Pattern Anal Mach Intell 42(7):1770\u20131782","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10589_CR13","unstructured":"Li Y-J, Lin C-S, Lin Y-B, Wang F Y-C (2019) Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation. In: 2019 IEEE International Conference on Computer Vision (ICCV) 7918-7928"},{"key":"10589_CR14","unstructured":"Lin Y-T., Dong X-Y, Zheng L, Yan Y, Yang Y (2019) A bottom-up clustering approach to unsupervised person re-identification. In: 2019 proceedings of the AAAI conference on artificial intelligence 33:8738-8745"},{"key":"10589_CR15","doi-asserted-by":"publisher","first-page":"5481","DOI":"10.1109\/TIP.2020.2982826","volume":"29","author":"Y Lin","year":"2020","unstructured":"Lin Y, Wu Y, Yan C, Xu M, Yang Y (2020) Unsupervised person re-identification via cross-camera similarity exploration. IEEE Trans Image Process 29:5481\u20135490","journal-title":"IEEE Trans Image Process"},{"key":"10589_CR16","doi-asserted-by":"publisher","first-page":"114021","DOI":"10.1109\/ACCESS.2019.2933910","volume":"7","author":"J Liu","year":"2019","unstructured":"Liu J, Li W, Pei H, Wang Y, Qu F, Qu Y, Chen Y (2019) Identity preserving generative adversarial network for cross-domain person re-identification. IEEE Access 7:114021\u2013114032","journal-title":"IEEE Access"},{"key":"10589_CR17","unstructured":"Pan X-G, Luo P, Shi J-P, Tang X-O (2018) Two at once: enhancing learning and generalization capacities via ibn-net. In: 2018 European conference on computer vision (ECCV) 484-500"},{"key":"10589_CR18","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1109\/TIFS.2019.2939750","volume":"15","author":"C-X Ren","year":"2020","unstructured":"Ren C-X, Liang B, Ge P, Zhai Y, Lei Z (2020) Domain adaptive person re-identification via camera style generation and label propagation. IEEE Trans Inf Forensics Secur 15:1290\u20131302","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"10","key":"10589_CR19","doi-asserted-by":"publisher","first-page":"3016","DOI":"10.1109\/TCSVT.2018.2872503","volume":"29","author":"C Shen","year":"2019","unstructured":"Shen C, Qi G-J, Jiang R, Jin Z, Yong H, Chen Y, Hua X-S (2019) Sharp attention network via adaptive sampling for person re-identification. IEEE Trans Circuits Syst Vid Technol 29(10):3016\u20133027","journal-title":"IEEE Trans Circuits Syst Vid Technol"},{"key":"10589_CR20","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.patrec.2019.11.032","volume":"129","author":"A Sikdar","year":"2020","unstructured":"Sikdar A, Chowdhury AS (2020) Scale-invariant batch-adaptive residual learning for person re-identification. Pattern Recogn Lett 129:279\u2013286","journal-title":"Pattern Recogn Lett"},{"key":"10589_CR21","doi-asserted-by":"crossref","unstructured":"Song L, Wang C, Zhang L, Du B, Zhang Q, Huang C, Wang X (2020) Unsupervised domain adaptive re-identification: Theory and practice Pattern Recognition 102","DOI":"10.1016\/j.patcog.2019.107173"},{"key":"10589_CR22","unstructured":"Wang J-Y, Zhu X-T, Gong S-G, Li W (2018) Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: 2018 proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) 2275-2284"},{"key":"10589_CR23","unstructured":"Wu A, Zheng W-S, Lai J-H (2019) Unsupervised person reidentification by camera-aware similarity consistency learning. In: 2019 proceedings of the IEEE international conference on computer vision (ECCV) 6922\u20136931"},{"issue":"8","key":"10589_CR24","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1049\/iet-cvi.2018.5103","volume":"12","author":"Y Xian","year":"2018","unstructured":"Xian Y, Hu H (2018) Enhanced multi-dataset transfer learning method for unsupervised person re-identification using co-training strategy. IET Comput Vis 12(8):1219\u20131227","journal-title":"IET Comput Vis"},{"issue":"6","key":"10589_CR25","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TCSVT.2015.2511543","volume":"27","author":"G-S Xie","year":"2017","unstructured":"Xie G-S, Zhang X-Y, Yan S, Liu C-L (2017) Hybrid CNN and dictionary-based models for scene recognition and domain adaptation. IEEE Trans Circuits Syst Vid Technol 27(6):1263\u20131274","journal-title":"IEEE Trans Circuits Syst Vid Technol"},{"key":"10589_CR26","first-page":"1","volume":"10","author":"G-S Xie","year":"2019","unstructured":"Xie G-S, Zhang Z, Liu L, Zhu F, Zhang X-Y, Shao L, Li X (2019) SRSC: selective, robust, and supervised constrained feature representation for image classification. IEEE Trans Neural Netw Learn Syst 10:1\u201313","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10589_CR27","doi-asserted-by":"crossref","unstructured":"Yang F, Yan K, Lu S, Jia H, Xie D, Yu Z, Guo X, Huang F, Gao W (2020) Part-aware progressive unsupervised domain adaptation for person re-identification. IEEE Trans Multimed, 1","DOI":"10.1109\/TMM.2020.3001522"},{"key":"10589_CR28","unstructured":"Yu H-Y, Zheng W-S, Wu A-C, et al (2019) Unsupervised person re-identification by soft multilabel learning. In: 2019 IEEE conference on computer vision and pattern recognition (CVPR) 2148-2157"},{"issue":"4","key":"10589_CR29","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1109\/TPAMI.2018.2886878","volume":"42","author":"H-X Yu","year":"2020","unstructured":"Yu H-X, Wu A, Zheng W-S (2020) Unsupervised person re-identification by deep asymmetric metric embedding. IEEE Trans Pattern Anal Mach Intell 42(4):956\u2013973","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10589_CR30","unstructured":"Zhang X-Y, Cao J-W, Shen C-H, You M-Y (2019) Self-training with progressive augmentation for unsupervised cross-domain person re-identification. In: 2019 IEEE international conference on computer vision (ICCV) 8222-8231"},{"key":"10589_CR31","doi-asserted-by":"crossref","unstructured":"Zhao F, Liao S-C, Xie G-S, Zhao J, Shao L (2020) Unsupervised domain adaptation with noise resistible mutual-training for person re-identification. In: 2020 European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-030-58621-8_31"},{"key":"10589_CR32","doi-asserted-by":"crossref","unstructured":"Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: a benchmark. In: 2015 IEEE international conference on computer vision (ICCV) 1116-1124","DOI":"10.1109\/ICCV.2015.133"},{"key":"10589_CR33","doi-asserted-by":"crossref","unstructured":"Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: 2017 IEEE international conference on computer vision (ICCV) 3774-3782","DOI":"10.1109\/ICCV.2017.405"},{"issue":"10","key":"10589_CR34","doi-asserted-by":"publisher","first-page":"3037","DOI":"10.1109\/TCSVT.2018.2873599","volume":"29","author":"Z Zheng","year":"2019","unstructured":"Zheng Z, Zheng L, Yang Y (2019) Pedestrian alignment network for large-scale person re-identification. IEEE Trans Circuits Syst Vid Technol 29(10):3037\u20133045","journal-title":"IEEE Trans Circuits Syst Vid Technol"},{"key":"10589_CR35","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Li S-Z, Yang Y (2018) Generalizing a person retrieval model hetero-and homogeneously. In: 2018 proceedings of the European conference on computer vision (ECCV) 172-188","DOI":"10.1007\/978-3-030-01261-8_11"},{"key":"10589_CR36","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Luo Z-M, Li S-Z, Yang Y. Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification. In: 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 598\u2013607","DOI":"10.1109\/CVPR.2019.00069"},{"key":"10589_CR37","unstructured":"Zhu J-Y, Park T, Isola P, et al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE international conference on computer vision (ICCV) 2242-2251"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10589-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10589-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10589-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T06:15:43Z","timestamp":1619504143000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10589-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,2]]},"references-count":37,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["10589"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10589-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,2,2]]},"assertion":[{"value":"1 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Yanfeng Li has received research grants from National Nature Science Foundation of China. Houjin Chen has research grants from Major Science and Technology Innovation Project of Shandong Province.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}