{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T07:48:45Z","timestamp":1777621725320,"version":"3.51.4"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10489-022-03640-y","type":"journal-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T16:03:07Z","timestamp":1654531387000},"page":"4109-4123","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Reinforced domain adaptation with attention and adversarial learning for unsupervised person Re-ID"],"prefix":"10.1007","volume":"53","author":[{"given":"Peiyi","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4375-1405","authenticated-orcid":false,"given":"Canlong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanping","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,6]]},"reference":[{"key":"3640_CR1","doi-asserted-by":"crossref","unstructured":"Deng W, Zheng L, Ye Q, Kang G, Yang Y, Jiao J (2018) Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 994\u20131003","DOI":"10.1109\/CVPR.2018.00110"},{"key":"3640_CR2","doi-asserted-by":"crossref","unstructured":"Wei L, Zhang S, Gao W, Tian Q (2018) Person transfer gan to bridge domain gap for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 79\u201388","DOI":"10.1109\/CVPR.2018.00016"},{"issue":"3","key":"3640_CR3","doi-asserted-by":"publisher","first-page":"1176","DOI":"10.1109\/TIP.2018.2874313","volume":"28","author":"Z Zhong","year":"2019","unstructured":"Zhong Z, Zheng L, Zheng Z, Li S, Yang Y (2019) Camstyle: A novel data augmentation method for person re-identification. IEEE Trans Image Process 28(3):1176\u20131190. https:\/\/doi.org\/10.1109\/TIP.2018.2874313","journal-title":"IEEE Trans Image Process"},{"key":"3640_CR4","doi-asserted-by":"crossref","unstructured":"Li Y-J, Lin C-S, Lin Y-B, Wang Y-C F (2019) Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 7918\u20137928","DOI":"10.1109\/ICCV.2019.00801"},{"key":"3640_CR5","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Li S, Yang Y (2018) Generalizing a person retrieval model hetero-and homogeneously. In: Proceedings of the European conference on computer vision (ECCV), pp 172\u2013188","DOI":"10.1007\/978-3-030-01261-8_11"},{"key":"3640_CR6","doi-asserted-by":"crossref","unstructured":"Zhu J-Y, Park T, Isola P, Efros A (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 2242\u20132251","DOI":"10.1109\/ICCV.2017.244"},{"key":"3640_CR7","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein gan"},{"key":"3640_CR8","doi-asserted-by":"publisher","unstructured":"Fan H, Zheng L, Yang Y (2017) Unsupervised person re-identification: Clustering and fine-tuning. ACM Trans Multimed Comput Commun Appl 14 https:\/\/doi.org\/10.1145\/3243316","DOI":"10.1145\/3243316"},{"key":"3640_CR9","doi-asserted-by":"crossref","unstructured":"Zhang X, Cao J, Shen C, You M (2019) Self-training with progressive augmentation for unsupervised cross-domain person re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 8221\u20138230","DOI":"10.1109\/ICCV.2019.00831"},{"key":"3640_CR10","doi-asserted-by":"crossref","unstructured":"Fu Y, Wei Y, Wang G, Zhou Y, Shi H, Uiuc U, Huang T (2019) Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 6111\u20136120","DOI":"10.1109\/ICCV.2019.00621"},{"key":"3640_CR11","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1109\/LSP.2020.3016528","volume":"27","author":"S Chen","year":"2020","unstructured":"Chen S, Fan Z, Yin J (2020) Pseudo label based on multiple clustering for unsupervised cross-domain person re-identification. IEEE Signal Process Lett 27:1460\u20131464. https:\/\/doi.org\/10.1109\/LSP.2020.3016528https:\/\/doi.org\/10.1109\/LSP.2020.3016528","journal-title":"IEEE Signal Process Lett"},{"key":"3640_CR12","doi-asserted-by":"publisher","first-page":"8738","DOI":"10.1609\/aaai.v33i01.33018738 10.1609\/aaai.v33i01.33018738","volume":"33","author":"Y Lin","year":"2019","unstructured":"Lin Y, Dong X, Zheng L, Yang Y (2019) A bottom-up clustering approach to unsupervised person re-identification. Proc AAAI Conf Artif Intell 33:8738\u20138745. https:\/\/doi.org\/10.1609\/aaai.v33i01.33018738https:\/\/doi.org\/10.1609\/aaai.v33i01.33018738","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3640_CR13","doi-asserted-by":"publisher","first-page":"12597","DOI":"10.1609\/aaai.v34i07.6950","volume":"34","author":"F Yang","year":"2020","unstructured":"Yang F, Li K, Zhong Z, Luo Z, Sun X, Cheng H, Guo X, Huang F, Ji R, Li S (2020) Asymmetric co-teaching for unsupervised cross-domain person re-identification. Proc AAAI Conf Artif Intell 34:12597\u201312604. https:\/\/doi.org\/10.1609\/aaai.v34i07.6950","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3640_CR14","doi-asserted-by":"crossref","unstructured":"Duan L, Xu D, Chang S-F (2012) Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp 1338\u20131345","DOI":"10.1109\/CVPR.2012.6247819"},{"issue":"4","key":"3640_CR15","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1109\/TPAMI.2018.2814042","volume":"41","author":"A Rozantsev","year":"2019","unstructured":"Rozantsev A, Salzmann M, Fua P (2019) Beyond sharing weights for deep domain adaptation. IEEE Trans Pattern Anal Mach Intell 41(4):801\u2013814. https:\/\/doi.org\/10.1109\/TPAMI.2018.2814042","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3640_CR16","doi-asserted-by":"crossref","unstructured":"Ghifary M, Kleijn W B, Zhang M (2014) Domain adaptive neural networks for object recognition. In: Pacific Rim international conference on artificial intelligence. Springer, pp 898\u2013904","DOI":"10.1007\/978-3-319-13560-1_76"},{"key":"3640_CR17","unstructured":"Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (201412) Deep domain confusion: Maximizing for domain invariance. arXiv:1412.3474"},{"issue":"1","key":"3640_CR18","first-page":"2096","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096\u20132030","journal-title":"J Mach Learn Res"},{"key":"3640_CR19","doi-asserted-by":"publisher","first-page":"2980","DOI":"10.1109\/TIP.2011.2134107","volume":"20","author":"B Geng","year":"2011","unstructured":"Geng B, Tao D (2011) Daml: Domain adaptation metric learning. IEEE Trans Image Process 20:2980\u20132989. https:\/\/doi.org\/10.1109\/TIP.2011.2134107https:\/\/doi.org\/10.1109\/TIP.2011.2134107","journal-title":"IEEE Trans Image Process"},{"key":"3640_CR20","doi-asserted-by":"crossref","unstructured":"Redko I, Habrard A, Sebban M (2017) Theoretical analysis of domain adaptation with optimal transport. In: Joint european conference on machine learning and knowledge discovery in databases. Springer, pp 737\u2013753","DOI":"10.1007\/978-3-319-71246-8_45"},{"key":"3640_CR21","unstructured":"Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transportation distances. Adv Neural Information Process Syst 26"},{"key":"3640_CR22","doi-asserted-by":"publisher","unstructured":"Tang Y (2020) Cgan-tm: A novel domain-to-domain transferring method for person re-identification. IEEE Trans Image Process PP. https:\/\/doi.org\/10.1109\/TIP.2020.2985545","DOI":"10.1109\/TIP.2020.2985545"},{"key":"3640_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMM.2021.3126404 10.1109\/TMM.2021.3126404","volume":"PP","author":"V Astha","year":"2021","unstructured":"Astha V, Venkata S, Wang Z, Satoh S, Shah R (2021) Unsupervised domain adaptation for person re-identification via individual-preserving and environmental-switching cyclic generation. IEEE Trans Multimed PP:1\u20131. https:\/\/doi.org\/10.1109\/TMM.2021.3126404https:\/\/doi.org\/10.1109\/TMM.2021.3126404","journal-title":"IEEE Trans Multimed"},{"key":"3640_CR24","unstructured":"Ge Y, Chen D, Li H (2020) Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification. arXiv:2001.01526"},{"key":"3640_CR25","doi-asserted-by":"crossref","unstructured":"Tay C-P, Roy S, Yap K-H (2019) Aanet: Attribute attention network for person re-identifications. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7127\u20137136","DOI":"10.1109\/CVPR.2019.00730"},{"key":"3640_CR26","doi-asserted-by":"crossref","unstructured":"Gao S, Wang J, Lu H, Zimo L (2020) Pose-guided visible part matching for occluded person reid. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 11741\u201311749","DOI":"10.1109\/CVPR42600.2020.01176"},{"key":"3640_CR27","doi-asserted-by":"publisher","unstructured":"Yang J, Zhang C, Tang Y, Li Z (2022) Pafm: pose-drive attention fusion mechanism for occluded person re-identification. Neural Comput Appl:1\u201312. https:\/\/doi.org\/10.1007\/s00521-022-06903-4","DOI":"10.1007\/s00521-022-06903-4"},{"key":"3640_CR28","doi-asserted-by":"crossref","unstructured":"Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X (2017) Residual attention network for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6450\u20136458","DOI":"10.1109\/CVPR.2017.683"},{"key":"3640_CR29","unstructured":"Taigman Y, Polyak A, Wolf L (2016) Unsupervised cross-domain image generation"},{"key":"3640_CR30","doi-asserted-by":"crossref","unstructured":"Villani C (2009) Optimal transport: old and new, vol 338. Springer","DOI":"10.1007\/978-3-540-71050-9"},{"key":"3640_CR31","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A (2017) Improved training of wasserstein gans"},{"key":"3640_CR32","unstructured":"Ester M, Kriegel H-P, Sander J, Xu X et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise.. In: kdd, vol 96, pp 226\u2013231"},{"key":"3640_CR33","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Sun G, Albanie S (2017) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell PP. https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"3640_CR34","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: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1116\u20131124","DOI":"10.1109\/ICCV.2015.133"},{"key":"3640_CR35","doi-asserted-by":"crossref","unstructured":"Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: European conference on computer vision, vol 9914","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"3640_CR36","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3640_CR37","unstructured":"Zhang H, Wu C, Zhang Z, Zhu Y, Lin H, Zhang Z, Sun Y, He T, Mueller J, Manmatha R et al (2020) Resnest: Split-attention networks. arXiv:2004.08955"},{"key":"3640_CR38","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Li F-F (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"3640_CR39","unstructured":"Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks"},{"key":"3640_CR40","doi-asserted-by":"publisher","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2017) Random erasing data augmentation. Proc AAAI Conf Artif Intell 34. https:\/\/doi.org\/10.1609\/aaai.v34i07.7000","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"3640_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2020.2976933","volume":"PP","author":"Z Zhong","year":"2020","unstructured":"Zhong Z, Zheng L, Luo Z, Li S, Yang Y (2020) Learning to adapt invariance in memory for person re-identification. IEEE Trans Pattern Anal Mach Intell PP:1\u20131. https:\/\/doi.org\/10.1109\/TPAMI.2020.2976933https:\/\/doi.org\/10.1109\/TPAMI.2020.2976933","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3640_CR42","doi-asserted-by":"crossref","unstructured":"Zou Y, Yang X, Yu Z, Kumar BVK, Kautz J (2020) Joint disentangling and adaptation for cross-domain person re-identification. In: European conference on computer vision. Springer, pp 87\u2013 104","DOI":"10.1007\/978-3-030-58536-5_6"},{"key":"3640_CR43","doi-asserted-by":"publisher","first-page":"107173","DOI":"10.1016\/j.patcog.2019.107173","volume":"102","author":"L Song","year":"2020","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 Recogn 102:107173. https:\/\/doi.org\/10.1016\/j.patcog.2019.107173https:\/\/doi.org\/10.1016\/j.patcog.2019.107173, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S003132031930473X","journal-title":"Pattern Recogn"},{"key":"3640_CR44","doi-asserted-by":"crossref","unstructured":"Yu H-X, Zheng W-S, Wu A, Guo X, Gong S, Lai J-H (2019) Unsupervised person re-identification by soft multilabel learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2143\u20132152","DOI":"10.1109\/CVPR.2019.00225"},{"key":"3640_CR45","doi-asserted-by":"crossref","unstructured":"Yuan Y, Chen W, Chen T, Yang Y, Ren Z, Wang Z, Hua G (2020) Calibrated domain-invariant learning for highly generalizable large scale re-identification. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 3578\u20133587","DOI":"10.1109\/WACV45572.2020.9093521"},{"key":"3640_CR46","doi-asserted-by":"crossref","unstructured":"Yang Q, Yu H-X, Wu A, Zheng W-S (2019) Patch-based discriminative feature learning for unsupervised person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3628\u20133637","DOI":"10.1109\/CVPR.2019.00375"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03640-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03640-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03640-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T06:42:55Z","timestamp":1675233775000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03640-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":46,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["3640"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03640-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]},"assertion":[{"value":"14 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}