{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:27:45Z","timestamp":1740122865614,"version":"3.37.3"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2022,3,19]],"date-time":"2022-03-19T00:00:00Z","timestamp":1647648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,19]],"date-time":"2022-03-19T00:00:00Z","timestamp":1647648000000},"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":"publisher","award":["61962046","62001255","61841204"],"award-info":[{"award-number":["61962046","62001255","61841204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Inner Mongolia Outstanding Youth Cultivation Fund","award":["2018JQ02"],"award-info":[{"award-number":["2018JQ02"]}]},{"DOI":"10.13039\/501100004763","name":"Inner Mongolia Natural Science Foundation","doi-asserted-by":"crossref","award":["2019MS06003"],"award-info":[{"award-number":["2019MS06003"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11042-022-12728-z","type":"journal-article","created":{"date-parts":[[2022,3,19]],"date-time":"2022-03-19T12:03:21Z","timestamp":1647691401000},"page":"24081-24098","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A novel unsupervised person re-identification algorithm based on soft multi-label and compound attention model"],"prefix":"10.1007","volume":"81","author":[{"given":"Zhang","family":"Baohua","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhu","family":"Siyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhou","family":"Yufeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Xiaoqi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gu","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Jianjun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liu","family":"Xin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,19]]},"reference":[{"key":"12728_CR1","doi-asserted-by":"crossref","unstructured":"Cheng D, Gong Y, Zhou S et al (2016) Person re-identification by multi-channel parts-based cnn with improved triplet loss function. Proc IEEE Conf Comput Vis Pattern Recognit:1335\u20131344","DOI":"10.1109\/CVPR.2016.149"},{"key":"12728_CR2","doi-asserted-by":"crossref","unstructured":"Deng W, Zheng L, Ye Q et al (2018) Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. Proc IEEE Conf Comput Vis Pattern Recognit:994\u20131003","DOI":"10.1109\/CVPR.2018.00110"},{"issue":"4","key":"12728_CR3","first-page":"83","volume":"14","author":"H Fan","year":"2018","unstructured":"Fan H, Zheng L, Yan C et al (2018) Unsupervised person re-identification: Clustering and fine-tuning. ACM Trans Multimedia Comput Commun Appl (TOMM) 14(4):83","journal-title":"ACM Trans Multimedia Comput Commun Appl (TOMM)"},{"key":"12728_CR4","doi-asserted-by":"crossref","unstructured":"Fu Y, Wei Y, Wang G, et al. (2019) Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification. Proceedings of the IEEE International Conference on Computer Vision, 6112\u20136121","DOI":"10.1109\/ICCV.2019.00621"},{"key":"12728_CR5","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. Proc IEEE Conf Comput Vis Pattern Recognit:770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7","key":"12728_CR6","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1109\/TPAMI.2018.2842770","volume":"41","author":"R He","year":"2018","unstructured":"He R, Wu X, Sun Z, Tan T (2018) Wasserstein cnn: learning invariant features for nir-Vis face recognition. IEEE Trans Pattern Anal Mach Intell 41(7):1761\u20131773","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"12728_CR7","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. Proc IEEE Conf Comput Vis Pattern Recognit:7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"11","key":"12728_CR8","doi-asserted-by":"publisher","first-page":"5012","DOI":"10.1109\/TIP.2016.2602079","volume":"25","author":"G Li","year":"2016","unstructured":"Li G, Yu Y (2016) Visual saliency detection based on multiscale deep CNN features. IEEE Trans Image Process 25(11):5012\u20135024","journal-title":"IEEE Trans Image Process"},{"key":"12728_CR9","doi-asserted-by":"crossref","unstructured":"Li Y J, Yang F E, Liu Y C, et al. (2018) Adaptation and re-identification network: An unsupervised deep transfer learning approach to person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 172\u2013178","DOI":"10.1109\/CVPRW.2018.00054"},{"key":"12728_CR10","doi-asserted-by":"crossref","unstructured":"Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identification. Proc IEEE Conf Comput Vis Pattern Recognit:2285\u20132294","DOI":"10.1109\/CVPR.2018.00243"},{"issue":"7","key":"12728_CR11","doi-asserted-by":"publisher","first-page":"1770","DOI":"10.1109\/TPAMI.2019.2903058","volume":"42","author":"M Li","year":"2019","unstructured":"Li M, Zhu X, Gong S (2019) Unsupervised Tracklet person re-identification. IEEE Trans Pattern Anal Mach Intell 42(7):1770\u20131782","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"12728_CR12","doi-asserted-by":"crossref","unstructured":"Li Y J, Lin C S, Lin Y B, et al. (2019) Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation. Proceedings of the IEEE International Conference on Computer Vision, 7919\u20137929","DOI":"10.1109\/ICCV.2019.00801"},{"key":"12728_CR13","volume-title":"Multi-task mid-level feature alignment network for unsupervised cross-dataset person re-identification","author":"S Lin","year":"2018","unstructured":"Lin S, Li H, Li CT et al (2018) Multi-task mid-level feature alignment network for unsupervised cross-dataset person re-identification. arXiv preprint arXiv:1807.01440"},{"key":"12728_CR14","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.patcog.2019.06.006","volume":"95","author":"Y Lin","year":"2019","unstructured":"Lin Y, Zheng L, Zheng Z, Wu Y, Hu Z, Yan C, Yang Y (2019) Improving person re-identification by attribute and identity learning. Pattern Recogn 95:151\u2013161","journal-title":"Pattern Recogn"},{"key":"12728_CR15","first-page":"8738","volume":"33","author":"Y Lin","year":"2019","unstructured":"Lin Y, Dong X, Zheng L, Yan Y, Yang Y (2019) A bottom-up clustering approach to unsupervised person re-identification. Proc AAAI Conf Artif Intell 33:8738\u20138745","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"12728_CR16","doi-asserted-by":"crossref","unstructured":"Liu C, Gong S, Loy C C, et al. (2012) Person re-identification: what features are important? European Conference on Computer Vision. Springer Berlin: Heidelberg, 391\u2013401","DOI":"10.1007\/978-3-642-33863-2_39"},{"key":"12728_CR17","volume-title":"On the variance of the adaptive learning rate and beyond","author":"L Liu","year":"2019","unstructured":"Liu L, Jiang H, He P et al (2019) On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv:1908.03265"},{"key":"12728_CR18","doi-asserted-by":"crossref","unstructured":"Song C, Huang Y, Ouyang W, et al. (2018) Mask-guided contrastive attention model for person re-identification. Comput Vis Pattern Recognit, 1179\u20131188","DOI":"10.1109\/CVPR.2018.00129"},{"key":"12728_CR19","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","journal-title":"Pattern Recogn"},{"issue":"2","key":"12728_CR20","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1109\/TCSVT.2016.2555739","volume":"28","author":"S Tan","year":"2016","unstructured":"Tan S, Zheng F, Liu L, Han J, Shao L (2016) Dense invariant feature-based support vector ranking for cross-camera person reidentification [J]. IEEE Trans Circ Sys Video Technol 28(2):356\u2013363","journal-title":"IEEE Trans Circ Sys Video Technol"},{"key":"12728_CR21","doi-asserted-by":"crossref","unstructured":"Wang J, Zhu X, Gong S et al (2018) Transferable joint attribute-identity deep learning for unsupervised person re-identification. Proc IEEE Conf Comput Vis Pattern Recognit:2275\u20132284","DOI":"10.1109\/CVPR.2018.00242"},{"key":"12728_CR22","doi-asserted-by":"crossref","unstructured":"Wei L, Zhang S, Gao W et al (2018) Person transfer Gan to bridge domain gap for person re-identification. Proc IEEE Conf Comput Vis Pattern Recognit:79\u201388","DOI":"10.1109\/CVPR.2018.00016"},{"key":"12728_CR23","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J Y, et al. (2018) CBAM: Convolutional block attention model. Proceedings of the European Conference on Computer Vision (ECCV), 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"12728_CR24","doi-asserted-by":"crossref","unstructured":"Wu J, Yang Y, Liu H, et al. (2019) Unsupervised graph association for person re-identification. Proceedings of the IEEE International Conference on Computer Vision, 8321\u20138330","DOI":"10.1109\/ICCV.2019.00841"},{"key":"12728_CR25","doi-asserted-by":"crossref","unstructured":"Wu A, Zheng W S, Lai J H (2019) Unsupervised person re-identification by camera-aware similarity consistency learning. Proceedings of the IEEE International Conference on Computer Vision, 6922\u20136931","DOI":"10.1109\/ICCV.2019.00702"},{"issue":"7","key":"12728_CR26","first-page":"2081","volume":"30","author":"L Wu","year":"2019","unstructured":"Wu L, Hong R, Wang Y, Wang M (2019) Cross-entropy adversarial view adaptation for person re-identification. IEEE Trans Circ Syst Video Technol 30(7):2081\u20132092","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"12728_CR27","doi-asserted-by":"crossref","unstructured":"Xiao T, Li H, Ouyang W et al (2016) Learning deep feature representations with domain guided dropout for person re-identification. Proc IEEE Conf Comput Vis Pattern Recognit:1249\u20131258","DOI":"10.1109\/CVPR.2016.140"},{"key":"12728_CR28","doi-asserted-by":"publisher","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":"12728_CR29","doi-asserted-by":"crossref","unstructured":"Xu J, Zhao R, Zhu F, et al. (2018) Attention-aware compositional network for person re-identification. Comput Vis Pattern Recognit, 2119\u20132128","DOI":"10.1109\/CVPR.2018.00226"},{"key":"12728_CR30","doi-asserted-by":"crossref","unstructured":"Yang Q, Yu H X, Wu A, et al. (2019) Patch-Based Discriminative Feature Learning for Unsupervised Person Re-Identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3633\u20133642.","DOI":"10.1109\/CVPR.2019.00375"},{"issue":"6","key":"12728_CR31","doi-asserted-by":"publisher","first-page":"2976","DOI":"10.1109\/TIP.2019.2893066","volume":"28","author":"M Ye","year":"2019","unstructured":"Ye M, Li J, Ma AJ, Zheng L, Yuen PC (2019) Dynamic graph co-matching for unsupervised video-based person re-identification. IEEE Trans Image Process 28(6):2976\u20132990","journal-title":"IEEE Trans Image Process"},{"key":"12728_CR32","doi-asserted-by":"crossref","unstructured":"Yu H X, Wu A, Zheng W S (2017) Cross-view asymmetric metric learning for unsupervised person re-identification. Proceedings of the IEEE International Conference on Computer Vision, 994\u20131002","DOI":"10.1109\/ICCV.2017.113"},{"issue":"4","key":"12728_CR33","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1109\/TPAMI.2018.2886878","volume":"42","author":"HX Yu","year":"2018","unstructured":"Yu HX, Wu A, Zheng WS (2018) 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":"12728_CR34","doi-asserted-by":"crossref","unstructured":"Yu H X, Zheng W S, Wu A, et al. (2019) Unsupervised Person Re-identification by Soft multi-label Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2148\u20132157","DOI":"10.1109\/CVPR.2019.00225"},{"key":"12728_CR35","doi-asserted-by":"crossref","unstructured":"Zeiler M D, Fergus R (2014) Visualizing and understanding convolutional networks. European conference on computer vision. Springer: Cham, 818\u2013833","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"12728_CR36","first-page":"1","volume":"32","author":"X Zhang","year":"2020","unstructured":"Zhang X, Jing XY, Zhu X, Ma F (2020) Semi-supervised person re-identification by similarity-embedded cycle GANs [J]. Neural Comput & Applic 32:1\u201310","journal-title":"Neural Comput & Applic"},{"key":"12728_CR37","doi-asserted-by":"crossref","unstructured":"Zhao H, Tian M, Sun S et al (2017) Spindle net: person re-identification with human body region guided feature decomposition and fusion. Proc IEEE Conf Comput Vis Pattern Recognit:1077\u20131085","DOI":"10.1109\/CVPR.2017.103"},{"key":"12728_CR38","doi-asserted-by":"crossref","unstructured":"Zheng L, Shen L, Tian L, et al. (2015) Scalable person re-identification: a benchmark. Proceedings of the IEEE International Conference on Computer Vision, 1116\u20131124","DOI":"10.1109\/ICCV.2015.133"},{"key":"12728_CR39","volume-title":"Person re-identification: Past, present and future","author":"L Zheng","year":"2016","unstructured":"Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: Past, present and future. arXiv preprint arXiv:1610.02984"},{"key":"12728_CR40","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. Proceedings of the IEEE International Conference on Computer Vision, 3754\u20133762","DOI":"10.1109\/ICCV.2017.405"},{"key":"12728_CR41","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Cao D et al (2017) Re-ranking person re-identification with k-reciprocal encoding. Proc IEEE Conf Comput Vis Pattern Recognit:1318\u20131327","DOI":"10.1109\/CVPR.2017.389"},{"key":"12728_CR42","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Li S, et al. (2018) Generalizing a person retrieval model hetero-and homogeneously. Proceedings of the European Conference on Computer Vision (ECCV), 172\u2013188.","DOI":"10.1007\/978-3-030-01261-8_11"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12728-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12728-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12728-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T08:31:04Z","timestamp":1656577864000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12728-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,19]]},"references-count":42,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["12728"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12728-z","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2022,3,19]]},"assertion":[{"value":"2 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}