{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:20:48Z","timestamp":1774466448977,"version":"3.50.1"},"reference-count":201,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62103350"],"award-info":[{"award-number":["62103350"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of Chin","doi-asserted-by":"crossref","award":["62072391"],"award-info":[{"award-number":["62072391"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of Chin","doi-asserted-by":"crossref","award":["62066013"],"award-info":[{"award-number":["62066013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of Chin","doi-asserted-by":"crossref","award":["62273290"],"award-info":[{"award-number":["62273290"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Pedestrian re-identification (re-ID) has gained considerable attention as a challenging research area in smart cities. Its applications span diverse domains, including intelligent transportation, public security, new retail, and the integration of face re-ID technology. The rapid progress in deep learning techniques, coupled with the availability of large-scale pedestrian datasets, has led to remarkable advancements in pedestrian re-ID. In this paper, we begin the study by summarising the key datasets and standard evaluation methodologies for pedestrian re-ID. Second, we look into pedestrian re-ID methods that are based on object re-ID, loss functions, research directions, weakly supervised classification, and various application scenarios. Moreover, we assess and display different re-ID approaches from deep learning perspectives. Finally, several challenges and future directions for pedestrian re-ID development are discussed. By providing a holistic perspective on this topic, this research serves as a valuable resource for researchers and practitioners, enabling further advancements in pedestrian re-ID within smart city environments.<\/jats:p>","DOI":"10.1007\/s40747-023-01229-7","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T04:01:21Z","timestamp":1696305681000},"page":"1733-1768","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A comprehensive review of pedestrian re-identification based on deep learning"],"prefix":"10.1007","volume":"10","author":[{"given":"Zhaojie","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7606-1411","authenticated-orcid":false,"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Youlei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yongchao","family":"Song","sequence":"additional","affiliation":[]},{"given":"Jindong","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jindong","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Weiqing","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Cuicui","family":"Lv","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"key":"1229_CR1","unstructured":"Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984"},{"key":"1229_CR2","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, pp 1116\u20131124","DOI":"10.1109\/ICCV.2015.133"},{"key":"1229_CR3","doi-asserted-by":"crossref","unstructured":"Martinel N, Luca\u00a0Foresti G, Micheloni C (2019) Aggregating deep pyramidal representations for person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops","DOI":"10.1109\/CVPRW.2019.00196"},{"key":"1229_CR4","doi-asserted-by":"crossref","first-page":"3908","DOI":"10.1109\/TIP.2022.3175593","volume":"31","author":"X Gu","year":"2022","unstructured":"Gu X, Chang H, Ma B, Shan S (2022) Motion feature aggregation for video-based person re-identification. IEEE Trans Image Process 31:3908\u20133919","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"1229_CR5","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1109\/TIP.2018.2878505","volume":"28","author":"J Dai","year":"2018","unstructured":"Dai J, Zhang P, Wang D, Lu H, Wang H (2018) Video person re-identification by temporal residual learning. IEEE Trans Image Process 28(3):1366\u20131377","journal-title":"IEEE Trans Image Process"},{"key":"1229_CR6","doi-asserted-by":"crossref","unstructured":"Ye M, Liang C, Wang Z, Leng Q, Chen J, Liu J (2015) Specific person retrieval via incomplete text description. In: Proceedings of the 5th ACM on international conference on multimedia retrieval, pp 547\u2013550","DOI":"10.1145\/2671188.2749347"},{"key":"1229_CR7","doi-asserted-by":"crossref","unstructured":"Haque A, Alahi A, Fei-Fei L (2016) Recurrent attention models for depth-based person identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1229\u20131238","DOI":"10.1109\/CVPR.2016.138"},{"key":"1229_CR8","doi-asserted-by":"crossref","unstructured":"Karianakis N, Liu Z, Chen Y, Soatto S (2018) Reinforced temporal attention and split-rate transfer for depth-based person re-identification. In: Proceedings of the European conference on computer vision (ECCV), pp 715\u2013733","DOI":"10.1007\/978-3-030-01228-1_44"},{"key":"1229_CR9","doi-asserted-by":"crossref","unstructured":"Li S, Xiao T, Li H, Zhou B, Yue D, Wang X (2017) Person search with natural language description. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1970\u20131979","DOI":"10.1109\/CVPR.2017.551"},{"key":"1229_CR10","doi-asserted-by":"crossref","unstructured":"Chen D, Li H, Liu X, Shen Y, Shao J, Yuan Z, Wang X (2018) Improving deep visual representation for person re-identification by global and local image-language association. In: Proceedings of the European conference on computer vision (ECCV), pp 54\u201370","DOI":"10.1007\/978-3-030-01270-0_4"},{"key":"1229_CR11","doi-asserted-by":"crossref","unstructured":"Ye M, Wang Z, Lan X, Yuen PC (2018) Visible thermal person re-identification via dual-constrained top-ranking. In: IJCAI, vol 1, p 2","DOI":"10.24963\/ijcai.2018\/152"},{"key":"1229_CR12","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang L, You Y, Zou X, Chen V, Li S, Huang G, Hariharan B, Weinberger KQ (2018) Resource aware person re-identification across multiple resolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8042\u20138051","DOI":"10.1109\/CVPR.2018.00839"},{"key":"1229_CR13","doi-asserted-by":"crossref","unstructured":"Karanam S, Li Y, Radke RJ (2015) Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: Proceedings of the IEEE international conference on computer vision, pp 4516\u20134524","DOI":"10.1109\/ICCV.2015.513"},{"key":"1229_CR14","doi-asserted-by":"crossref","unstructured":"Song C, Huang Y, Ouyang W, Wang L (2018) Mask-guided contrastive attention model for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1179\u20131188","DOI":"10.1109\/CVPR.2018.00129"},{"key":"1229_CR15","doi-asserted-by":"crossref","unstructured":"Miao J, Wu Y, Liu P, Ding Y, Yang Y (2019) Pose-guided feature alignment for occluded person re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 542\u2013551","DOI":"10.1109\/ICCV.2019.00063"},{"key":"1229_CR16","doi-asserted-by":"crossref","unstructured":"Wu A, Zheng W-S, Yu H-X, Gong S, Lai J (2017) Rgb-infrared cross-modality person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 5380\u20135389","DOI":"10.1109\/ICCV.2017.575"},{"key":"1229_CR17","doi-asserted-by":"crossref","unstructured":"Huang Y, Zha Z-J, Fu X, Zhang W (2019) Illumination-invariant person re-identification. In: Proceedings of the 27th ACM international conference on multimedia, pp 365\u2013373","DOI":"10.1145\/3343031.3350994"},{"key":"1229_CR18","doi-asserted-by":"crossref","unstructured":"Qian X, Wang W, Zhang L, Zhu F, Fu Y, Xiang T, Jiang Y-G, Xue X (2020) Long-term cloth-changing person re-identification. In: Proceedings of the Asian conference on computer vision","DOI":"10.1007\/978-3-030-69535-4_5"},{"key":"1229_CR19","doi-asserted-by":"crossref","unstructured":"Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: European conference on computer vision. Springer, pp 262\u2013275","DOI":"10.1007\/978-3-540-88682-2_21"},{"key":"1229_CR20","doi-asserted-by":"crossref","unstructured":"Liao S, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2197\u20132206","DOI":"10.1109\/CVPR.2015.7298832"},{"key":"1229_CR21","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: Proceedings of the IEEE international conference on computer vision, pp 3754\u20133762","DOI":"10.1109\/ICCV.2017.405"},{"key":"1229_CR22","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: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 152\u2013159","DOI":"10.1109\/CVPR.2014.27"},{"key":"1229_CR23","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.neucom.2019.01.079","volume":"337","author":"D Wu","year":"2019","unstructured":"Wu D, Zheng S-J, Zhang X-P, Yuan C-A, Cheng F, Zhao Y, Lin Y-J, Zhao Z-Q, Jiang Y-L, Huang D-S (2019) Deep learning-based methods for person re-identification: a comprehensive review. Neurocomputing 337:354\u2013371","journal-title":"Neurocomputing"},{"key":"1229_CR24","doi-asserted-by":"crossref","first-page":"175228","DOI":"10.1109\/ACCESS.2019.2957336","volume":"7","author":"MO Almasawa","year":"2019","unstructured":"Almasawa MO, Elrefaei LA, Moria K (2019) A survey on deep learning-based person re-identification systems. IEEE Access 7:175228\u2013175247","journal-title":"IEEE Access"},{"key":"1229_CR25","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2022.104394","volume":"119","author":"Z Ming","year":"2022","unstructured":"Ming Z, Zhu M, Wang X, Zhu J, Cheng J, Gao C, Yang Y, Wei X (2022) Deep learning-based person re-identification methods: a survey and outlook of recent works. Image Vis Comput 119:104394","journal-title":"Image Vis Comput"},{"key":"1229_CR26","volume":"16","author":"A Gupta","year":"2022","unstructured":"Gupta A, Pawade P, Balakrishnan R (2022) Deep residual network and transfer learning-based person re-identification. Intell Syst Appl 16:200137","journal-title":"Intell Syst Appl"},{"issue":"12","key":"1229_CR27","volume":"8","author":"D Wu","year":"2022","unstructured":"Wu D, Huang H, Zhao Q, Zhang S, Qi J, Hu J (2022) Overview of deep learning based pedestrian attribute recognition and re-identification. Heliyon 8(12):e12086","journal-title":"Heliyon"},{"key":"1229_CR28","first-page":"1","volume":"2","author":"W-S Zheng","year":"2009","unstructured":"Zheng W-S, Gong S, Xiang T (2009) Associating groups of people. BMVC 2:1\u201311","journal-title":"BMVC"},{"key":"1229_CR29","doi-asserted-by":"crossref","unstructured":"Hirzer M, Beleznai C, Roth PM, Bischof H (2011) Person re-identification by descriptive and discriminative classification. In: Scandinavian conference on image analysis. Springer, pp 91\u2013102","DOI":"10.1007\/978-3-642-21227-7_9"},{"key":"1229_CR30","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, pp 79\u201388","DOI":"10.1109\/CVPR.2018.00016"},{"key":"1229_CR31","doi-asserted-by":"crossref","unstructured":"Wang T, Gong S, Zhu X, Wang S (2014) Person re-identification by video ranking. In: European conference on computer vision. Springer, pp 688\u2013703","DOI":"10.1007\/978-3-319-10593-2_45"},{"key":"1229_CR32","doi-asserted-by":"crossref","unstructured":"Zheng L, Bie Z, Sun Y, Wang J, Su C, Wang S, Tian Q (2016) Mars: A video benchmark for large-scale person re-identification. In: European conference on computer vision. Springer, pp 868\u2013884","DOI":"10.1007\/978-3-319-46466-4_52"},{"key":"1229_CR33","doi-asserted-by":"crossref","unstructured":"Wu Y, Lin Y, Dong X, Yan Y, Ouyang W, Yang Y (2018) Exploit the unknown gradually: one-shot video-based person re-identification by stepwise learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5177\u20135186","DOI":"10.1109\/CVPR.2018.00543"},{"issue":"3","key":"1229_CR34","doi-asserted-by":"crossref","first-page":"605","DOI":"10.3390\/s17030605","volume":"17","author":"DT Nguyen","year":"2017","unstructured":"Nguyen DT, Hong HG, Kim KW, Park KR (2017) Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors 17(3):605","journal-title":"Sensors"},{"key":"1229_CR35","doi-asserted-by":"crossref","unstructured":"Barbosa IB, Cristani M, Bue AD, Bazzani L, Murino V (2012) Re-identification with rgb-d sensors. In: European conference on computer vision. Springer, pp 433\u2013442","DOI":"10.1007\/978-3-642-33863-2_43"},{"key":"1229_CR36","doi-asserted-by":"crossref","unstructured":"Munaro M, Basso A, Fossati A, Van\u00a0Gool L, Menegatti E (2014) 3d reconstruction of freely moving persons for re-identification with a depth sensor. In: 2014 IEEE international conference on robotics and automation (ICRA). IEEE, pp 4512\u20134519","DOI":"10.1109\/ICRA.2014.6907518"},{"key":"1229_CR37","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1162\/tacl_a_00166","volume":"2","author":"P Young","year":"2014","unstructured":"Young P, Lai A, Hodosh M, Hockenmaier J (2014) From image descriptions to visual denotations: new similarity metrics for semantic inference over event descriptions. Trans Assoc Comput Linguist 2:67\u201378","journal-title":"Trans Assoc Comput Linguist"},{"issue":"8","key":"1229_CR38","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1109\/TCSVT.2016.2515309","volume":"27","author":"Y-C Chen","year":"2016","unstructured":"Chen Y-C, Zheng W-S, Lai J-H, Yuen PC (2016) An asymmetric distance model for cross-view feature mapping in person reidentification. IEEE Trans Circuits Syst Video Technol 27(8):1661\u20131675","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"2","key":"1229_CR39","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303\u2013338","journal-title":"Int J Comput Vis"},{"key":"1229_CR40","doi-asserted-by":"crossref","unstructured":"Wang F, Zuo W, Lin L, Zhang D, Zhang L (2016) Joint learning of single-image and cross-image representations for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1288\u20131296","DOI":"10.1109\/CVPR.2016.144"},{"key":"1229_CR41","doi-asserted-by":"crossref","unstructured":"Chen G, Lin C, Ren L, Lu J, Zhou J (2019) Self-critical attention learning for person re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9637\u20139646","DOI":"10.1109\/ICCV.2019.00973"},{"key":"1229_CR42","unstructured":"Xia BN, Gong Y, Zhang Y, Poellabauer C (2019) Second-order non-local attention networks for person re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3760\u20133769"},{"key":"1229_CR43","doi-asserted-by":"crossref","unstructured":"Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European conference on computer vision (ECCV), pp 480\u2013496","DOI":"10.1007\/978-3-030-01225-0_30"},{"key":"1229_CR44","doi-asserted-by":"crossref","unstructured":"Zhang Z, Zhang H, Liu S (2021) Person re-identification using heterogeneous local graph attention networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12136\u201312145","DOI":"10.1109\/CVPR46437.2021.01196"},{"key":"1229_CR45","doi-asserted-by":"crossref","unstructured":"Sarfraz MS, Schumann A, Eberle A, Stiefelhagen R (2018) A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 420\u2013429","DOI":"10.1109\/CVPR.2018.00051"},{"key":"1229_CR46","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 7134\u20137143","DOI":"10.1109\/CVPR.2019.00730"},{"key":"1229_CR47","doi-asserted-by":"crossref","unstructured":"Zhu Z, Jiang X, Zheng F, Guo X, Huang F, Sun X, Zheng W (2020) Aware loss with angular regularization for person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 13114\u201313121","DOI":"10.1609\/aaai.v34i07.7014"},{"key":"1229_CR48","doi-asserted-by":"crossref","unstructured":"Wang Y, Chen Z, Wu F, Wang G (2018) Person re-identification with cascaded pairwise convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1470\u20131478","DOI":"10.1109\/CVPR.2018.00159"},{"key":"1229_CR49","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":"1229_CR50","first-page":"11309","volume":"33","author":"Y Ge","year":"2020","unstructured":"Ge Y, Zhu F, Chen D, Zhao R et al (2020) Self-paced contrastive learning with hybrid memory for domain adaptive object re-id. Adv Neural Inf Process Syst 33:11309\u201311321","journal-title":"Adv Neural Inf Process Syst"},{"key":"1229_CR51","doi-asserted-by":"crossref","unstructured":"Xuan S, Zhang S (2021) Intra-inter camera similarity for unsupervised person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11926\u201311935","DOI":"10.1109\/CVPR46437.2021.01175"},{"key":"1229_CR52","doi-asserted-by":"crossref","unstructured":"Chen H, Wang Y, Lagadec B, Dantcheva A, Bremond F (2021) Joint generative and contrastive learning for unsupervised person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2004\u20132013","DOI":"10.1109\/CVPR46437.2021.00204"},{"key":"1229_CR53","doi-asserted-by":"crossref","unstructured":"Zheng K, Liu W, He L, Mei T, Luo J, Zha Z-J (2021) Group-aware label transfer for domain adaptive person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5310\u20135319","DOI":"10.1109\/CVPR46437.2021.00527"},{"key":"1229_CR54","doi-asserted-by":"crossref","unstructured":"McLaughlin N, Del\u00a0Rincon JM, Miller P (2016) Recurrent convolutional network for video-based person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1325\u20131334","DOI":"10.1109\/CVPR.2016.148"},{"key":"1229_CR55","doi-asserted-by":"crossref","unstructured":"Xu S, Cheng Y, Gu K, Yang Y, Chang S, Zhou P (2017) Jointly attentive spatial-temporal pooling networks for video-based person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 4733\u20134742","DOI":"10.1109\/ICCV.2017.507"},{"key":"1229_CR56","doi-asserted-by":"crossref","unstructured":"Chen D, Li H, Xiao T, Yi S, Wang X (2018) Video person re-identification with competitive snippet-similarity aggregation and co-attentive snippet embedding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1169\u20131178","DOI":"10.1109\/CVPR.2018.00128"},{"key":"1229_CR57","doi-asserted-by":"crossref","unstructured":"Liu X, Zhang P, Yu C, Lu H, Yang X (2021) Watching you: global-guided reciprocal learning for video-based person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13334\u201313343","DOI":"10.1109\/CVPR46437.2021.01313"},{"key":"1229_CR58","doi-asserted-by":"crossref","unstructured":"Hao Y, Wang N, Li J, Gao X (2019) Hsme: hypersphere manifold embedding for visible thermal person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8385\u20138392","DOI":"10.1609\/aaai.v33i01.33018385"},{"key":"1229_CR59","doi-asserted-by":"crossref","unstructured":"Choi S, Lee S, Kim Y, Kim T, Kim C (2020) Hi-cmd: Hierarchical cross-modality disentanglement for visible-infrared person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10257\u201310266","DOI":"10.1109\/CVPR42600.2020.01027"},{"key":"1229_CR60","doi-asserted-by":"crossref","unstructured":"Ye M, Shen J, J\u00a0Crandall D, Shao L, Luo J (2020) Dynamic dual-attentive aggregation learning for visible-infrared person re-identification. In: European conference on computer vision. Springer, pp 229\u2013247","DOI":"10.1007\/978-3-030-58520-4_14"},{"key":"1229_CR61","doi-asserted-by":"crossref","unstructured":"Chen Y, Wan L, Li Z, Jing Q, Sun Z (2021) Neural feature search for rgb-infrared person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 587\u2013597","DOI":"10.1109\/CVPR46437.2021.00065"},{"key":"1229_CR62","doi-asserted-by":"crossref","unstructured":"Ye M, Lan X, Li J, Yuen P (2018) Hierarchical discriminative learning for visible thermal person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.12293"},{"issue":"6","key":"1229_CR63","doi-asserted-by":"crossref","first-page":"2588","DOI":"10.1109\/TIP.2017.2675201","volume":"26","author":"A Wu","year":"2017","unstructured":"Wu A, Zheng W-S, Lai J-H (2017) Robust depth-based person re-identification. IEEE Trans Image Process 26(6):2588\u20132603","journal-title":"IEEE Trans Image Process"},{"key":"1229_CR64","doi-asserted-by":"crossref","unstructured":"Zhang Y, Lu H (2018) Deep cross-modal projection learning for image-text matching. In: Proceedings of the European conference on computer vision (ECCV), pp 686\u2013701","DOI":"10.1007\/978-3-030-01246-5_42"},{"key":"1229_CR65","doi-asserted-by":"crossref","unstructured":"Liu J, Zha Z-J, Hong R, Wang M, Zhang Y (2019) Deep adversarial graph attention convolution network for text-based person search. In: Proceedings of the 27th ACM international conference on multimedia, pp 665\u2013673","DOI":"10.1145\/3343031.3350991"},{"key":"1229_CR66","doi-asserted-by":"crossref","unstructured":"Wang Z, Ye M, Yang F, Bai X, 0001 SS (2018) Cascaded sr-gan for scale-adaptive low resolution person re-identification. In: IJCAI, vol 1, p 4","DOI":"10.24963\/ijcai.2018\/541"},{"key":"1229_CR67","doi-asserted-by":"crossref","unstructured":"Li Y-J, Chen Y-C, Lin Y-Y, Du X, Wang Y-CF (2019) Recover and identify: a generative dual model for cross-resolution person re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8090\u20138099","DOI":"10.1109\/ICCV.2019.00818"},{"key":"1229_CR68","doi-asserted-by":"crossref","unstructured":"Zhang G, Chen Y, Lin W, Chandran A, Jing X (2021) Low resolution information also matters: learning multi-resolution representations for person re-identification. arXiv preprint arXiv:2105.12684","DOI":"10.24963\/ijcai.2021\/179"},{"key":"1229_CR69","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"1229_CR70","doi-asserted-by":"crossref","unstructured":"Zhu K, Guo H, Liu Z, Tang M, Wang J (2020) Identity-guided human semantic parsing for person re-identification. In: European conference on computer vision. Springer, pp 346\u2013363","DOI":"10.1007\/978-3-030-58580-8_21"},{"issue":"16","key":"1229_CR71","doi-asserted-by":"crossref","first-page":"4431","DOI":"10.3390\/s20164431","volume":"20","author":"Q Yang","year":"2020","unstructured":"Yang Q, Wang P, Fang Z, Lu Q (2020) Focus on the visible regions: semantic-guided alignment model for occluded person re-identification. Sensors 20(16):4431","journal-title":"Sensors"},{"key":"1229_CR72","volume":"124","author":"T Si","year":"2022","unstructured":"Si T, He F, Wu H, Duan Y (2022) Spatial-driven features based on image dependencies for person re-identification. Pattern Recogn 124:108462","journal-title":"Pattern Recogn"},{"issue":"3","key":"1229_CR73","volume":"60","author":"J Yang","year":"2023","unstructured":"Yang J, Zhang C, Li Z, Tang Y, Wang Z (2023) Discriminative feature mining with relation regularization for person re-identification. Inf Process Manage 60(3):103295","journal-title":"Inf Process Manage"},{"issue":"12","key":"1229_CR74","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.1109\/TPAMI.2016.2522418","volume":"38","author":"T Wang","year":"2016","unstructured":"Wang T, Gong S, Zhu X, Wang S (2016) Person re-identification by discriminative selection in video ranking. IEEE Trans Pattern Anal Mach Intell 38(12):2501\u20132514","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"1229_CR75","doi-asserted-by":"crossref","first-page":"5683","DOI":"10.1109\/TIP.2018.2861366","volume":"27","author":"X Zhu","year":"2018","unstructured":"Zhu X, Jing X-Y, You X, Zhang X, Zhang T (2018) Video-based person re-identification by simultaneously learning intra-video and inter-video distance metrics. IEEE Trans Image Process 27(11):5683\u20135695","journal-title":"IEEE Trans Image Process"},{"key":"1229_CR76","doi-asserted-by":"crossref","unstructured":"You J, Wu A, Li X, Zheng W-S (2016) Top-push video-based person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1345\u20131353","DOI":"10.1109\/CVPR.2016.150"},{"key":"1229_CR77","doi-asserted-by":"crossref","unstructured":"Hou R, Chang H, Ma B, Huang R, Shan S (2021) Bicnet-tks: learning efficient spatial-temporal representation for video person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2014\u20132023","DOI":"10.1109\/CVPR46437.2021.00205"},{"key":"1229_CR78","doi-asserted-by":"crossref","unstructured":"Aich A, Zheng M, Karanam S, Chen T, Roy-Chowdhury AK, Wu Z (2021) Spatio-temporal representation factorization for video-based person re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 152\u2013162","DOI":"10.1109\/ICCV48922.2021.00022"},{"key":"1229_CR79","doi-asserted-by":"crossref","unstructured":"Bai S, Ma B, Chang H, Huang R, Chen X (2022) Salient-to-broad transition for video person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7339\u20137348","DOI":"10.1109\/CVPR52688.2022.00719"},{"key":"1229_CR80","volume":"129","author":"Y Yao","year":"2022","unstructured":"Yao Y, Jiang X, Fujita H, Fang Z (2022) A sparse graph wavelet convolution neural network for video-based person re-identification. Pattern Recogn 129:108708","journal-title":"Pattern Recogn"},{"issue":"9","key":"1229_CR81","doi-asserted-by":"crossref","first-page":"6100","DOI":"10.1109\/TCSVT.2022.3157130","volume":"32","author":"C Chen","year":"2022","unstructured":"Chen C, Ye M, Qi M, Wu J, Liu Y, Jiang J (2022) Saliency and granularity: discovering temporal coherence for video-based person re-identification. IEEE Trans Circuits Syst Video Technol 32(9):6100\u20136112","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1229_CR82","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3236144","author":"J Lu","year":"2023","unstructured":"Lu J, Wan H, Li P, Zhao X, Ma N, Gao Y (2023) Exploring high-order spatio-temporal correlations from skeleton for person re-identification. IEEE Trans Image Process. https:\/\/doi.org\/10.1109\/TIP.2023.3236144","journal-title":"IEEE Trans Image Process"},{"key":"1229_CR83","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei S-E, Sheikh Y (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7291\u20137299","DOI":"10.1109\/CVPR.2017.143"},{"key":"1229_CR84","doi-asserted-by":"crossref","unstructured":"Insafutdinov E, Pishchulin L, Andres B, Andriluka M, Schiele B (2016) Deepercut: a deeper, stronger, and faster multi-person pose estimation model. In: European conference on computer vision. Springer, pp 34\u201350","DOI":"10.1007\/978-3-319-46466-4_3"},{"key":"1229_CR85","doi-asserted-by":"crossref","unstructured":"Wei S-E, Ramakrishna V, Kanade T, Sheikh Y (2016) Convolutional pose machines. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4724\u20134732","DOI":"10.1109\/CVPR.2016.511"},{"key":"1229_CR86","doi-asserted-by":"crossref","unstructured":"Fu Y, Wei Y, Zhou Y, Shi H, Huang G, Wang X, Yao Z, Huang T (2019) Horizontal pyramid matching for person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8295\u20138302","DOI":"10.1609\/aaai.v33i01.33018295"},{"key":"1229_CR87","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3151487","author":"K Zhu","year":"2022","unstructured":"Zhu K, Guo H, Liu S, Wang J, Tang M (2022) Learning semantics-consistent stripes with self-refinement for person re-identification. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2022.3151487","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1229_CR88","doi-asserted-by":"crossref","unstructured":"Li D, Chen X, Zhang Z, Huang K (2017) Learning deep context-aware features over body and latent parts for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 384\u2013393","DOI":"10.1109\/CVPR.2017.782"},{"key":"1229_CR89","doi-asserted-by":"crossref","unstructured":"Chen B, Deng W, Hu J (2019) Mixed high-order attention network for person re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 371\u2013381","DOI":"10.1109\/ICCV.2019.00046"},{"key":"1229_CR90","doi-asserted-by":"crossref","unstructured":"Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285\u20132294","DOI":"10.1109\/CVPR.2018.00243"},{"key":"1229_CR91","doi-asserted-by":"crossref","unstructured":"Si J, Zhang H, Li C-G, Kuen J, Kong X, Kot AC, Wang G (2018) Dual attention matching network for context-aware feature sequence based person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5363\u20135372","DOI":"10.1109\/CVPR.2018.00562"},{"key":"1229_CR92","doi-asserted-by":"crossref","unstructured":"Liu X, Zhao H, Tian M, Sheng L, Shao J, Yi S, Yan J, Wang X (2017) Hydraplus-net: attentive deep features for pedestrian analysis. In: Proceedings of the IEEE international conference on computer vision, pp 350\u2013359","DOI":"10.1109\/ICCV.2017.46"},{"key":"1229_CR93","doi-asserted-by":"crossref","unstructured":"Zhao L, Li X, Zhuang Y, Wang J (2017) Deeply-learned part-aligned representations for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 3219\u20133228","DOI":"10.1109\/ICCV.2017.349"},{"key":"1229_CR94","doi-asserted-by":"crossref","unstructured":"Zheng W-S, Li X, Xiang T, Liao S, Lai J, Gong S (2015) Partial person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 4678\u20134686","DOI":"10.1109\/ICCV.2015.531"},{"key":"1229_CR95","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.neucom.2020.05.106","volume":"453","author":"X Ning","year":"2021","unstructured":"Ning X, Gong K, Li W, Zhang L (2021) Jwsaa: joint weak saliency and attention aware for person re-identification. Neurocomputing 453:801\u2013811","journal-title":"Neurocomputing"},{"key":"1229_CR96","doi-asserted-by":"crossref","unstructured":"Guo J, Yuan Y, Huang L, Zhang C, Yao J-G, Han K (2019) Beyond human parts: Dual part-aligned representations for person re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3642\u20133651","DOI":"10.1109\/ICCV.2019.00374"},{"key":"1229_CR97","doi-asserted-by":"crossref","unstructured":"Liu J, Ni B, Yan Y, Zhou P, Cheng S, Hu J (2018) Pose transferrable person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4099\u20134108","DOI":"10.1109\/CVPR.2018.00431"},{"key":"1229_CR98","doi-asserted-by":"crossref","unstructured":"Yi D, Lei Z, Liao S, Li SZ (2014) Deep metric learning for person re-identification. In: 2014 22nd International conference on pattern recognition. IEEE, pp 34\u201339","DOI":"10.1109\/ICPR.2014.16"},{"key":"1229_CR99","doi-asserted-by":"crossref","unstructured":"Kalayeh MM, Basaran E, G\u00f6kmen M, Kamasak ME, Shah M (2018) Human semantic parsing for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1062\u20131071","DOI":"10.1109\/CVPR.2018.00117"},{"key":"1229_CR100","doi-asserted-by":"crossref","unstructured":"Wang G, Yuan Y, Chen X, Li J, Zhou X (2018) Learning discriminative features with multiple granularities for person re-identification. In: Proceedings of the 26th ACM international conference on multimedia, pp 274\u2013282","DOI":"10.1145\/3240508.3240552"},{"key":"1229_CR101","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1109\/TIFS.2022.3146773","volume":"17","author":"M Zhang","year":"2022","unstructured":"Zhang M, Xiao Y, Xiong F, Li S, Cao Z, Fang Z, Zhou JT (2022) Person re-identification with hierarchical discriminative spatial aggregation. IEEE Trans Inf Forensics Secur 17:516\u2013530","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"1229_CR102","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3207949","author":"Q Xie","year":"2022","unstructured":"Xie Q, Lu Z, Zhou W, Li H (2022) Improving person re-identification with multi-cue similarity embedding and propagation. IEEE Trans Multimedia. https:\/\/doi.org\/10.1109\/TMM.2022.3207949","journal-title":"IEEE Trans Multimedia"},{"key":"1229_CR103","volume":"134","author":"J Xi","year":"2023","unstructured":"Xi J, Huang J, Zheng S, Zhou Q, Schiele B, Hua X-S, Sun Q (2023) Learning comprehensive global features in person re-identification: ensuring discriminativeness of more local regions. Pattern Recogn 134:109068","journal-title":"Pattern Recogn"},{"issue":"1","key":"1229_CR104","first-page":"1","volume":"14","author":"Z Zheng","year":"2017","unstructured":"Zheng Z, Zheng L, Yang Y (2017) A discriminatively learned cnn embedding for person reidentification. ACM Trans Multimedia Comput Commun Appl (TOMM) 14(1):1\u201320","journal-title":"ACM Trans Multimedia Comput Commun Appl (TOMM)"},{"key":"1229_CR105","unstructured":"Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737"},{"issue":"27","key":"1229_CR106","doi-asserted-by":"crossref","first-page":"39169","DOI":"10.1007\/s11042-022-13170-x","volume":"81","author":"B Yang","year":"2022","unstructured":"Yang B, Shan Y, Peng R, Li J, Chen S, Li L (2022) A feature extraction method for person re-identification based on a two-branch cnn. Multimedia Tools Appl 81(27):39169\u201339184","journal-title":"Multimedia Tools Appl"},{"key":"1229_CR107","doi-asserted-by":"crossref","unstructured":"Su C, Li J, Zhang S, Xing J, Gao W, Tian Q (2017) Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 3960\u20133969","DOI":"10.1109\/ICCV.2017.427"},{"key":"1229_CR108","doi-asserted-by":"crossref","unstructured":"Fang P, Zhou J, Roy SK, Petersson L, Harandi M (2019) Bilinear attention networks for person retrieval. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8030\u20138039","DOI":"10.1109\/ICCV.2019.00812"},{"key":"1229_CR109","doi-asserted-by":"crossref","unstructured":"Fu Y, Wang X, Wei Y, Huang T (2019) Sta: Spatial-temporal attention for large-scale video-based person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8287\u20138294","DOI":"10.1609\/aaai.v33i01.33018287"},{"key":"1229_CR110","doi-asserted-by":"crossref","unstructured":"Li S, Bak S, Carr P, Wang X (2018) Diversity regularized spatiotemporal attention for video-based person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 369\u2013378","DOI":"10.1109\/CVPR.2018.00046"},{"key":"1229_CR111","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1229_CR112","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 3156\u20133164","DOI":"10.1109\/CVPR.2017.683"},{"key":"1229_CR113","doi-asserted-by":"crossref","unstructured":"Li Y, He J, Zhang T, Liu X, Zhang Y, Wu F (2021) Diverse part discovery: occluded person re-identification with part-aware transformer. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2898\u20132907","DOI":"10.1109\/CVPR46437.2021.00292"},{"key":"1229_CR114","doi-asserted-by":"crossref","unstructured":"Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285\u20132294","DOI":"10.1109\/CVPR.2018.00243"},{"key":"1229_CR115","doi-asserted-by":"crossref","unstructured":"He S, Luo H, Wang P, Wang F, Li H, Jiang W (2021) Transreid: Transformer-based object re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 15013\u201315022","DOI":"10.1109\/ICCV48922.2021.01474"},{"key":"1229_CR116","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.patrec.2018.08.024","volume":"127","author":"Y Yan","year":"2019","unstructured":"Yan Y, Ni B, Liu J, Yang X (2019) Multi-level attention model for person re-identification. Pattern Recogn Lett 127:156\u2013164","journal-title":"Pattern Recogn Lett"},{"key":"1229_CR117","doi-asserted-by":"crossref","unstructured":"He L, Liang J, Li H, Sun Z (2018) Deep spatial feature reconstruction for partial person re-identification: alignment-free approach. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7073\u20137082","DOI":"10.1109\/CVPR.2018.00739"},{"key":"1229_CR118","doi-asserted-by":"crossref","unstructured":"Jia M, Cheng X, Zhai Y, Lu S, Ma S, Tian Y, Zhang J (2021) Matching on sets: conquer occluded person re-identification without alignment. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35, pp 1673\u20131681","DOI":"10.1609\/aaai.v35i2.16260"},{"key":"1229_CR119","doi-asserted-by":"crossref","unstructured":"Sun Y, Xu Q, Li Y, Zhang C, Li Y, Wang S, Sun J (2019) Perceive where to focus: learning visibility-aware part-level features for partial person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 393\u2013402","DOI":"10.1109\/CVPR.2019.00048"},{"key":"1229_CR120","doi-asserted-by":"crossref","unstructured":"Gu X, Chang H, Ma B, Zhang H, Chen X (2020) Appearance-preserving 3d convolution for video-based person re-identification. In: European conference on computer vision. Springer, pp 228\u2013243","DOI":"10.1007\/978-3-030-58536-5_14"},{"key":"1229_CR121","doi-asserted-by":"crossref","unstructured":"Qian X, Fu Y, Xiang T, Wang W, Qiu J, Wu Y, Jiang Y-G, Xue X (2018) Pose-normalized image generation for person re-identification. In: Proceedings of the European conference on computer vision (ECCV), pp 650\u2013667","DOI":"10.1007\/978-3-030-01240-3_40"},{"key":"1229_CR122","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434"},{"key":"1229_CR123","doi-asserted-by":"crossref","unstructured":"Cheng D, Gong Y, Zhou S, Wang J, Zheng N (2016) Person re-identification by multi-channel parts-based cnn with improved triplet loss function. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1335\u20131344","DOI":"10.1109\/CVPR.2016.149"},{"issue":"1s","key":"1229_CR124","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3491225","volume":"18","author":"Z Zhao","year":"2022","unstructured":"Zhao Z, Song R, Zhang Q, Duan P, Zhang Y (2022) Jot-gan: a framework for jointly training gan and person re-identification model. ACM Trans Multimedia Comput Commun Appl (TOMM) 18(1s):1\u201318","journal-title":"ACM Trans Multimedia Comput Commun Appl (TOMM)"},{"issue":"8","key":"1229_CR125","doi-asserted-by":"crossref","first-page":"4096","DOI":"10.1109\/TCSVT.2023.3240001","volume":"33","author":"G Zhang","year":"2023","unstructured":"Zhang G, Zhang H, Lin W, Chandran AK, Jing X (2023) Camera contrast learning for unsupervised person re-identification. IEEE Trans Circuits Syst Video Technol 33(8):4096\u20134107","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1229_CR126","unstructured":"Elyor K, Xiang T, Fu Z, Gong S (2016) Person re-identification by unsupervised l1 graph learning. In: Proceedings of the 14th European conference on computer vision (ECCV), Amsterdam, The Netherlands, pp 8\u201316"},{"key":"1229_CR127","doi-asserted-by":"crossref","unstructured":"Liu Z, Wang D, Lu H (2017) Stepwise metric promotion for unsupervised video person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 2429\u20132438","DOI":"10.1109\/ICCV.2017.266"},{"key":"1229_CR128","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Luo Z, Li S, Yang Y (2019) Invariance matters: exemplar memory for domain adaptive person re-identification. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp 598\u2013607","DOI":"10.1109\/CVPR.2019.00069"},{"key":"1229_CR129","doi-asserted-by":"crossref","unstructured":"Zheng K, Lan C, Zeng W, Zhang Z, Zha Z-J (2021) Exploiting sample uncertainty for domain adaptive person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 3538\u20133546","DOI":"10.1609\/aaai.v35i4.16468"},{"key":"1229_CR130","doi-asserted-by":"crossref","unstructured":"Dai Y, Li X, Liu J, Tong Z, Duan L-Y (2021) Generalizable person re-identification with relevance-aware mixture of experts. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16145\u201316154","DOI":"10.1109\/CVPR46437.2021.01588"},{"key":"1229_CR131","doi-asserted-by":"crossref","unstructured":"He T, Shen L, Guo Y, Ding G, Guo Z (2022) Secret: Self-consistent pseudo label refinement for unsupervised domain adaptive person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 879\u2013887","DOI":"10.1609\/aaai.v36i1.19970"},{"key":"1229_CR132","doi-asserted-by":"crossref","unstructured":"Zheng Y, Tang S, Teng G, Ge Y, Liu K, Qin J, Qi D, Chen D (2021) Online pseudo label generation by hierarchical cluster dynamics for adaptive person re-identification. In: Proceedings of the IEEE\/cvf international conference on computer vision, pp 8371\u20138381","DOI":"10.1109\/ICCV48922.2021.00826"},{"key":"1229_CR133","unstructured":"Dai Z, Wang G, Yuan W, Liu X, Zhu S, Tan P (2021) Cluster contrast for unsupervised person re-identification. arXiv preprint arXiv:2103.11568"},{"issue":"4","key":"1229_CR134","doi-asserted-by":"crossref","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 Multimedia Comput Commun Appl (TOMM) 14(4):1\u201318","journal-title":"ACM Trans Multimedia Comput Commun Appl (TOMM)"},{"key":"1229_CR135","doi-asserted-by":"crossref","unstructured":"Yang Y, Wen L, Lyu S, Li S (2017) Unsupervised learning of multi-level descriptors for person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 31","DOI":"10.1609\/aaai.v31i1.11224"},{"key":"1229_CR136","doi-asserted-by":"crossref","unstructured":"Lin Y, Dong X, Zheng L, Yan Y, Yang Y (2019) A bottom-up clustering approach to unsupervised person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8738\u20138745","DOI":"10.1609\/aaai.v33i01.33018738"},{"key":"1229_CR137","doi-asserted-by":"crossref","first-page":"3606","DOI":"10.1109\/TIP.2022.3173163","volume":"31","author":"M Li","year":"2022","unstructured":"Li M, Li C-G, Guo J (2022) Cluster-guided asymmetric contrastive learning for unsupervised person re-identification. IEEE Trans Image Process 31:3606\u20133617","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"1229_CR138","doi-asserted-by":"crossref","first-page":"7494","DOI":"10.1109\/TPAMI.2022.3226866","volume":"45","author":"H Chen","year":"2022","unstructured":"Chen H, Wang Y, Lagadec B, Dantcheva A, Bremond F (2022) Learning invariance from generated variance for unsupervised person re-identification. IEEE Trans Pattern Anal Mach Intell 45(6):7494\u20137508","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"1229_CR139","volume":"60","author":"T Si","year":"2023","unstructured":"Si T, He F, Li P, Song Y, Fan L (2023) Diversity feature constraint based on heterogeneous data for unsupervised person re-identification. Inf Process Manage 60(3):103304","journal-title":"Inf Process Manage"},{"key":"1229_CR140","volume":"138","author":"F Chen","year":"2023","unstructured":"Chen F, Wang N, Tang J, Yan P, Yu J (2023) Unsupervised person re-identification via multi-domain joint learning. Pattern Recogn 138:109369","journal-title":"Pattern Recogn"},{"issue":"3","key":"1229_CR141","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3089249","volume":"13","author":"X Yang","year":"2017","unstructured":"Yang X, Wang M, Hong R, Tian Q, Rui Y (2017) Enhancing person re-identification in a self-trained subspace. ACM Trans Multimedia Comput Commun Appl (TOMM) 13(3):1\u201323","journal-title":"ACM Trans Multimedia Comput Commun Appl (TOMM)"},{"issue":"3","key":"1229_CR142","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1109\/TIP.2018.2874715","volume":"28","author":"Y Huang","year":"2018","unstructured":"Huang Y, Xu J, Wu Q, Zheng Z, Zhang Z, Zhang J (2018) Multi-pseudo regularized label for generated data in person re-identification. IEEE Trans Image Process 28(3):1391\u20131403","journal-title":"IEEE Trans Image Process"},{"key":"1229_CR143","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.patcog.2018.11.025","volume":"88","author":"X Xin","year":"2019","unstructured":"Xin X, Wang J, Xie R, Zhou S, Huang W, Zheng N (2019) Semi-supervised person re-identification using multi-view clustering. Pattern Recogn 88:285\u2013297","journal-title":"Pattern Recogn"},{"key":"1229_CR144","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.neucom.2022.11.009","volume":"518","author":"J Wu","year":"2023","unstructured":"Wu J, Yang Y, Lei Z, Yang Y, Chen S, Li SZ (2023) Camera-aware representation learning for person re-identification. Neurocomputing 518:155\u2013164","journal-title":"Neurocomputing"},{"key":"1229_CR145","doi-asserted-by":"crossref","unstructured":"Paisitkriangkrai S, Shen C, Van Den\u00a0Hengel A (2015) Learning to rank in person re-identification with metric ensembles. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1846\u20131855","DOI":"10.1109\/CVPR.2015.7298794"},{"key":"1229_CR146","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9729\u20139738","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"1229_CR147","unstructured":"Hjelm RD, Fedorov A, Lavoie-Marchildon S, Grewal K, Bachman P, Trischler A, Bengio Y (2018) Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670"},{"key":"1229_CR148","doi-asserted-by":"crossref","unstructured":"Wu Z, Xiong Y, Yu SX, Lin D (2018) Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3733\u20133742","DOI":"10.1109\/CVPR.2018.00393"},{"key":"1229_CR149","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2022.104607","volume":"129","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Shu Q, Shi X (2023) Dual-level contrastive learning for unsupervised person re-identification. Image Vis Comput 129:104607","journal-title":"Image Vis Comput"},{"issue":"1","key":"1229_CR150","first-page":"1","volume":"19","author":"D Liu","year":"2023","unstructured":"Liu D, Wu L, Hong R, Ge Z, Shen J, Boussaid F, Bennamoun M (2023) Generative metric learning for adversarially robust open-world person re-identification. ACM Trans Multimedia Comput Commun Appl 19(1):1\u201319","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"issue":"3","key":"1229_CR151","doi-asserted-by":"crossref","first-page":"3049","DOI":"10.1007\/s11042-017-5009-y","volume":"77","author":"F Zhu","year":"2018","unstructured":"Zhu F, Kong X, Wu Q, Fu H, Li M (2018) A loss combination based deep model for person re-identification. Multimedia Tools Appl 77(3):3049\u20133069","journal-title":"Multimedia Tools Appl"},{"key":"1229_CR152","doi-asserted-by":"crossref","unstructured":"Chen W, Chen X, Zhang J, Huang K (2017) Beyond triplet loss: a deep quadruplet network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 403\u2013412","DOI":"10.1109\/CVPR.2017.145"},{"key":"1229_CR153","doi-asserted-by":"crossref","unstructured":"Lin C-S, Wang Y-CF (2021) Self-supervised bodymap-to-appearance co-attention for partial person re-identification. In: 2021 IEEE international conference on image processing (ICIP). IEEE, pp 2299\u20132303","DOI":"10.1109\/ICIP42928.2021.9506470"},{"key":"1229_CR154","doi-asserted-by":"crossref","unstructured":"He Y, Yang H, Chen L (2021) Adversarial cross-scale alignment pursuit for seriously misaligned person re-identification. In: 2021 IEEE international conference on image processing (ICIP). IEEE, pp 2373\u20132377","DOI":"10.1109\/ICIP42928.2021.9506293"},{"key":"1229_CR155","doi-asserted-by":"crossref","unstructured":"Chen P, Liu W, Dai P, Liu J, Ye Q, Xu M, Chen Q, Ji R (2021) Occlude them all: occlusion-aware attention network for occluded person re-id. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 11833\u201311842","DOI":"10.1109\/ICCV48922.2021.01162"},{"key":"1229_CR156","doi-asserted-by":"crossref","unstructured":"Zhuo J, Chen Z, Lai J, Wang G (2018) Occluded person re-identification. In: 2018 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1\u20136","DOI":"10.1109\/ICME.2018.8486568"},{"key":"1229_CR157","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhu F, Tang S, Zhao R, He L, Song J (2022) Feature erasing and diffusion network for occluded person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4754\u20134763","DOI":"10.1109\/CVPR52688.2022.00471"},{"issue":"14","key":"1229_CR158","doi-asserted-by":"crossref","first-page":"11817","DOI":"10.1007\/s00521-022-07071-1","volume":"34","author":"L Zhang","year":"2022","unstructured":"Zhang L, Jiang N, Diao Q, Zhou Z, Wu W (2022) Person re-identification with pose variation aware data augmentation. Neural Comput Appl 34(14):11817\u201311830","journal-title":"Neural Comput Appl"},{"key":"1229_CR159","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.neucom.2021.11.013","volume":"470","author":"Y Shi","year":"2022","unstructured":"Shi Y, Ling H, Wu L, Zhang B, Li P (2022) Attribute disentanglement and registration for occluded person re-identification. Neurocomputing 470:226\u2013235","journal-title":"Neurocomputing"},{"key":"1229_CR160","doi-asserted-by":"crossref","unstructured":"G\u00fcler RA, Neverova N, Kokkinos I (2018) Densepose: Dense human pose estimation in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7297\u20137306","DOI":"10.1109\/CVPR.2018.00762"},{"key":"1229_CR161","doi-asserted-by":"crossref","unstructured":"Kim M, Cho M, Lee H, Cho S, Lee S (2022) Occluded person re-identification via relational adaptive feature correction learning. In: ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2719\u20132723","DOI":"10.1109\/ICASSP43922.2022.9746734"},{"key":"1229_CR162","doi-asserted-by":"crossref","unstructured":"Hou R, Ma B, Chang H, Gu X, Shan S, Chen X (2019) Vrstc: occlusion-free video person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7183\u20137192","DOI":"10.1109\/CVPR.2019.00735"},{"key":"1229_CR163","doi-asserted-by":"crossref","first-page":"4651","DOI":"10.1109\/TIP.2022.3186759","volume":"31","author":"B Xu","year":"2022","unstructured":"Xu B, He L, Liang J, Sun Z (2022) Learning feature recovery transformer for occluded person re-identification. IEEE Trans Image Process 31:4651\u20134662","journal-title":"IEEE Trans Image Process"},{"key":"1229_CR164","doi-asserted-by":"crossref","unstructured":"Somers V, De\u00a0Vleeschouwer C, Alahi A (2023) Body part-based representation learning for occluded person re-identification. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 1613\u20131623","DOI":"10.1109\/WACV56688.2023.00166"},{"key":"1229_CR165","unstructured":"Jing X-Y, Zhu X, Wu F, You X, Liu Q, Yue D, Hu R, Xu B (2015) Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning. In: Proceedings of the ieee conference on computer vision and pattern recognition, pp 695\u2013704"},{"key":"1229_CR166","doi-asserted-by":"crossref","unstructured":"Jiao J, Zheng W-S, Wu A, Zhu X, Gong S (2018) Deep low-resolution person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.12284"},{"key":"1229_CR167","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Husz\u00e1r F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681\u20134690","DOI":"10.1109\/CVPR.2017.19"},{"key":"1229_CR168","doi-asserted-by":"crossref","unstructured":"Cheng Z, Dong Q, Gong S, Zhu X (2020) Inter-task association critic for cross-resolution person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2605\u20132615","DOI":"10.1109\/CVPR42600.2020.00268"},{"key":"1229_CR169","doi-asserted-by":"crossref","first-page":"8913","DOI":"10.1109\/TIP.2021.3120054","volume":"30","author":"G Zhang","year":"2021","unstructured":"Zhang G, Ge Y, Dong Z, Wang H, Zheng Y, Chen S (2021) Deep high-resolution representation learning for cross-resolution person re-identification. IEEE Trans Image Process 30:8913\u20138925","journal-title":"IEEE Trans Image Process"},{"key":"1229_CR170","doi-asserted-by":"crossref","unstructured":"Gong Y, Huang L, Chen L (2022) Person re-identification method based on color attack and joint defence. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4313\u20134322","DOI":"10.1109\/CVPRW56347.2022.00477"},{"key":"1229_CR171","doi-asserted-by":"crossref","unstructured":"Wang G-A, Zhang T, Yang Y, Cheng J, Chang J, Liang X, Hou Z-G (2020) Cross-modality paired-images generation for rgb-infrared person re-identification. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 12144\u201312151","DOI":"10.1609\/aaai.v34i07.6894"},{"key":"1229_CR172","doi-asserted-by":"crossref","unstructured":"Dai P, Ji R, Wang H, Wu Q, Huang Y (2018) Cross-modality person re-identification with generative adversarial training. In: IJCAI, vol 1, p 6","DOI":"10.24963\/ijcai.2018\/94"},{"key":"1229_CR173","doi-asserted-by":"crossref","unstructured":"Hao X, Zhao S, Ye M, Shen J (2021) Cross-modality person re-identification via modality confusion and center aggregation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 16403\u201316412","DOI":"10.1109\/ICCV48922.2021.01609"},{"key":"1229_CR174","doi-asserted-by":"crossref","unstructured":"Liu J, Sun Y, Zhu F, Pei H, Yang Y, Li W (2022) Learning memory-augmented unidirectional metrics for cross-modality person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 19366\u201319375","DOI":"10.1109\/CVPR52688.2022.01876"},{"key":"1229_CR175","doi-asserted-by":"crossref","unstructured":"Alehdaghi M, Josi A, Cruz RM, Granger E (2023) Visible-infrared person re-identification using privileged intermediate information. In: Computer vision\u2013ECCV 2022 workshops, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part V. Springer, pp 720\u2013737","DOI":"10.1007\/978-3-031-25072-9_48"},{"issue":"6","key":"1229_CR176","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1109\/TPAMI.2019.2960509","volume":"43","author":"Q Yang","year":"2019","unstructured":"Yang Q, Wu A, Zheng W-S (2019) Person re-identification by contour sketch under moderate clothing change. IEEE Trans Pattern Anal Mach Intell 43(6):2029\u20132046","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1229_CR177","doi-asserted-by":"crossref","unstructured":"Zhang Z, Tran L, Yin X, Atoum Y, Liu X, Wan J, Wang N (2019) Gait recognition via disentangled representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4710\u20134719","DOI":"10.1109\/CVPR.2019.00484"},{"issue":"4","key":"1229_CR178","first-page":"1","volume":"18","author":"J Wu","year":"2022","unstructured":"Wu J, Jiang J, Qi M, Chen C, Zhang J (2022) An end-to-end heterogeneous restraint network for rgb-d cross-modal person re-identification. ACM Trans Multimedia Comput Commun Appl (TOMM) 18(4):1\u201322","journal-title":"ACM Trans Multimedia Comput Commun Appl (TOMM)"},{"key":"1229_CR179","doi-asserted-by":"crossref","unstructured":"Shao Z, Zhang X, Fang M, Lin Z, Wang J, Ding C (2022) Learning granularity-unified representations for text-to-image person re-identification. In: Proceedings of the 30th acm international conference on multimedia, pp 5566\u20135574","DOI":"10.1145\/3503161.3548028"},{"key":"1229_CR180","doi-asserted-by":"crossref","unstructured":"Fan L, Li T, Fang R, Hristov R, Yuan Y, Katabi D (2020) Learning longterm representations for person re-identification using radio signals. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10699\u201310709","DOI":"10.1109\/CVPR42600.2020.01071"},{"key":"1229_CR181","doi-asserted-by":"crossref","unstructured":"Jin X, He T, Zheng K, Yin Z, Shen X, Huang Z, Feng R, Huang J, Chen Z, Hua X-S (2022) Cloth-changing person re-identification from a single image with gait prediction and regularization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14278\u201314287","DOI":"10.1109\/CVPR52688.2022.01388"},{"key":"1229_CR182","doi-asserted-by":"crossref","unstructured":"Gu X, Chang H, Ma B, Bai S, Shan S, Chen X (2022) Clothes-changing person re-identification with rgb modality only. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1060\u20131069","DOI":"10.1109\/CVPR52688.2022.00113"},{"key":"1229_CR183","doi-asserted-by":"crossref","unstructured":"Hong P, Wu T, Wu A, Han X, Zheng W-S (2021) Fine-grained shape-appearance mutual learning for cloth-changing person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10513\u201310522","DOI":"10.1109\/CVPR46437.2021.01037"},{"issue":"4","key":"1229_CR184","doi-asserted-by":"crossref","first-page":"770","DOI":"10.3390\/pr10040770","volume":"10","author":"X Lu","year":"2022","unstructured":"Lu X, Li X, Sheng W, Ge SS (2022) Long-term person re-identification based on appearance and gait feature fusion under covariate changes. Processes 10(4):770","journal-title":"Processes"},{"key":"1229_CR185","doi-asserted-by":"crossref","unstructured":"Wu J, Liu H, Shi W, Tang H, Guo J (2022) Identity-sensitive knowledge propagation for cloth-changing person re-identification. In: 2022 IEEE international conference on image processing (ICIP). IEEE, pp 1016\u20131020","DOI":"10.1109\/ICIP46576.2022.9897243"},{"key":"1229_CR186","volume":"134","author":"R Zhang","year":"2023","unstructured":"Zhang R, Fang Y, Song H, Wan F, Fu Y, Kato H, Wu Y (2023) Specialized re-ranking: a novel retrieval-verification framework for cloth changing person re-identification. Pattern Recogn 134:109070","journal-title":"Pattern Recogn"},{"key":"1229_CR187","doi-asserted-by":"crossref","unstructured":"Chao H, He Y, Zhang J, Feng J (2019) Gaitset: regarding gait as a set for cross-view gait recognition. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8126\u20138133","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"1229_CR188","doi-asserted-by":"crossref","unstructured":"Fan C, Peng Y, Cao C, Liu X, Hou S, Chi J, Huang Y, Li Q, He Z (2020) Gaitpart: temporal part-based model for gait recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14225\u201314233","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"1229_CR189","doi-asserted-by":"crossref","unstructured":"Yu S, Li S, Chen D, Zhao R, Yan J, Qiao Y (2020) Cocas: a large-scale clothes changing person dataset for re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3400\u20133409","DOI":"10.1109\/CVPR42600.2020.00346"},{"key":"1229_CR190","doi-asserted-by":"crossref","first-page":"4227","DOI":"10.1109\/TIP.2022.3183469","volume":"31","author":"X Jia","year":"2022","unstructured":"Jia X, Zhong X, Ye M, Liu W, Huang W (2022) Complementary data augmentation for cloth-changing person re-identification. IEEE Trans Image Process 31:4227\u20134239","journal-title":"IEEE Trans Image Process"},{"key":"1229_CR191","doi-asserted-by":"crossref","unstructured":"Cai H, Wang Z, Cheng J (2019) Multi-scale body-part mask guided attention for person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops","DOI":"10.1109\/CVPRW.2019.00197"},{"key":"1229_CR192","doi-asserted-by":"crossref","first-page":"4482","DOI":"10.1109\/TMM.2021.3119133","volume":"24","author":"Z Yu","year":"2021","unstructured":"Yu Z, Zhao Y, Hong B, Jin Z, Huang J, Cai D, He X, Hua X-S (2021) Apparel-invariant feature learning for person re-identification. IEEE Trans Multimedia 24:4482-4492","journal-title":"IEEE Trans Multimedia"},{"key":"1229_CR193","doi-asserted-by":"crossref","unstructured":"Cho Y, Kim WJ, Hong S, Yoon S-E (2022) Part-based pseudo label refinement for unsupervised person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7308\u20137318","DOI":"10.1109\/CVPR52688.2022.00716"},{"key":"1229_CR194","doi-asserted-by":"crossref","unstructured":"Zhang X, Ge Y, Qiao Y, Li H (2021) Refining pseudo labels with clustering consensus over generations for unsupervised object re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3436\u20133445","DOI":"10.1109\/CVPR46437.2021.00344"},{"key":"1229_CR195","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 2148\u20132157","DOI":"10.1109\/CVPR.2019.00225"},{"key":"1229_CR196","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":"1229_CR197","doi-asserted-by":"crossref","unstructured":"Yan Y, Qin J, Chen J, Liu L, Zhu F, Tai Y, Shao L (2020) Learning multi-granular hypergraphs for video-based person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2899\u20132908","DOI":"10.1109\/CVPR42600.2020.00297"},{"key":"1229_CR198","doi-asserted-by":"crossref","first-page":"8821","DOI":"10.1109\/TIP.2020.3001693","volume":"29","author":"Y Wu","year":"2020","unstructured":"Wu Y, Bourahla OEF, Li X, Wu F, Tian Q, Zhou X (2020) Adaptive graph representation learning for video person re-identification. IEEE Trans Image Process 29:8821\u20138830","journal-title":"IEEE Trans Image Process"},{"key":"1229_CR199","doi-asserted-by":"crossref","first-page":"4240","DOI":"10.1109\/TIP.2022.3181811","volume":"31","author":"T Liu","year":"2022","unstructured":"Liu T, Lin Y, Du B (2022) Unsupervised person re-identification with stochastic training strategy. IEEE Trans Image Process 31:4240\u20134250","journal-title":"IEEE Trans Image Process"},{"key":"1229_CR200","doi-asserted-by":"crossref","unstructured":"Wang M, Li J, Lai B, Gong X, Hua X-S (2022) Offline-online associated camera-aware proxies for unsupervised person re-identification. arXiv preprint arXiv:2201.05820","DOI":"10.1109\/TIP.2022.3213193"},{"key":"1229_CR201","doi-asserted-by":"crossref","unstructured":"Zhang X, Li D, Wang Z, Wang J, Ding E, Shi JQ, Zhang Z, Wang J (2022) Implicit sample extension for unsupervised person re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7369\u20137378","DOI":"10.1109\/CVPR52688.2022.00722"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01229-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01229-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01229-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T21:56:42Z","timestamp":1730239002000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01229-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,3]]},"references-count":201,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["1229"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01229-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,3]]},"assertion":[{"value":"28 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}