{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:38:02Z","timestamp":1726043882993},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030305079"},{"type":"electronic","value":"9783030305086"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30508-6_23","type":"book-chapter","created":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T19:02:47Z","timestamp":1567969367000},"page":"281-293","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Global-Local Architecture Constrained by Multiple Attributes for Person Re-identification"],"prefix":"10.1007","author":[{"given":"Chao","family":"Liu","sequence":"first","affiliation":[]},{"given":"Hongyang","family":"Quan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","unstructured":"Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3908\u20133916 (2015). \n                      https:\/\/doi.org\/10.1109\/CVPR.2015.7299016","DOI":"10.1109\/CVPR.2015.7299016"},{"issue":"10","key":"23_CR2","doi-asserted-by":"publisher","first-page":"2993","DOI":"10.1016\/j.patcog.2015.04.005","volume":"48","author":"S Ding","year":"2015","unstructured":"Ding, S., Lin, L., Wang, G., Chao, H.: Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn. 48(10), 2993\u20133003 (2015). \n                      https:\/\/doi.org\/10.1016\/j.patcog.2015.04.005","journal-title":"Pattern Recogn."},{"key":"23_CR3","doi-asserted-by":"publisher","unstructured":"Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2360\u20132367. IEEE (2010). \n                      https:\/\/doi.org\/10.1109\/CVPR.2010.5539926","DOI":"10.1109\/CVPR.2010.5539926"},{"key":"23_CR4","unstructured":"Geng, M., Wang, Y., Xiang, T., Tian, Y.: Deep transfer learning for person re-identification. arXiv preprint \n                      arXiv:1611.05244\n                      \n                     (2016)"},{"key":"23_CR5","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778 (2016). \n                      https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR6","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint \n                      arXiv:1703.07737\n                      \n                     (2017)"},{"key":"23_CR7","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017\u20132025 (2015)"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2288\u20132295. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247939"},{"key":"23_CR9","doi-asserted-by":"publisher","unstructured":"Li, W., Zhu, X., Gong, S.: Person re-identification by deep joint learning of multi-loss classification, pp. 2194\u20132200 (2017). \n                      https:\/\/doi.org\/10.24963\/ijcai.2017\/305","DOI":"10.24963\/ijcai.2017\/305"},{"key":"23_CR10","doi-asserted-by":"publisher","unstructured":"Liao, S., Hu, Y., Zhu, X., Li, S.Z.: 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 (2015). \n                      https:\/\/doi.org\/10.1109\/CVPR.2015.7298832","DOI":"10.1109\/CVPR.2015.7298832"},{"key":"23_CR11","unstructured":"Lin, Y., Zheng, L., Zheng, Z., Wu, Y., Yang, Y.: Improving person re-identification by attribute and identity learning. arXiv preprint \n                      arXiv:1703.07220\n                      \n                     (2017)"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Matsukawa, T., Suzuki, E.: Person re-identification using cnn features learned from combination of attributes. In: International Conference on Pattern Recognition, pp. 2428\u20132433 (2016). \n                      https:\/\/doi.org\/10.1109\/ICPR.2016.7900000","DOI":"10.1109\/ICPR.2016.7900000"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Schumann, A., Stiefelhagen, R.: Person re-identification by deep learning attribute-complementary information. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 20\u201328 (2017)","DOI":"10.1109\/CVPRW.2017.186"},{"key":"23_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/978-3-030-01418-6_63","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2018","author":"C Sun","year":"2018","unstructured":"Sun, C., Jiang, N., Zhang, L., Wang, Y., Wu, W., Zhou, Z.: Unified framework for joint attribute classification and person re-identification. In: K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 637\u2013647. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-030-01418-6_63"},{"key":"23_CR15","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-030-01225-0_30","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Yifan Sun","year":"2018","unstructured":"Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline), pp. 501\u2013518 (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-030-01225-0_30"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Wang, G., Yuan, Y., Chen, X., Li, J., Zhou, X.: Learning discriminative features with multiple granularities for person re-identification. In: 2018 ACM Multimedia Conference on Multimedia Conference, pp. 274\u2013282. ACM (2018)","DOI":"10.1145\/3240508.3240552"},{"key":"23_CR17","doi-asserted-by":"publisher","unstructured":"Wei, L., Zhang, S., Yao, H., Gao, W., Tian, Q.: Glad: global-local-alignment descriptor for pedestrian retrieval. In: Proceedings of the 2017 ACM on Multimedia Conference, pp. 420\u2013428. ACM (2017). \n                      https:\/\/doi.org\/10.1145\/3123266.3123279","DOI":"10.1145\/3123266.3123279"},{"key":"23_CR18","unstructured":"Xiao, Q., Luo, H., Zhang, C.: Margin sample mining loss: a deep learning based method for person re-identification. arXiv preprint \n                      arXiv:1710.00478\n                      \n                     (2017)"},{"key":"23_CR19","doi-asserted-by":"publisher","unstructured":"Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1249\u20131258 (2016). \n                      https:\/\/doi.org\/10.1109\/CVPR.2016.140","DOI":"10.1109\/CVPR.2016.140"},{"key":"23_CR20","unstructured":"Zhang, X., et al.: AlignedReID: surpassing human-level performance in person re-identification. arXiv preprint \n                      arXiv:1711.08184\n                      \n                     (2017)"},{"key":"23_CR21","doi-asserted-by":"publisher","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116\u20131124 (2015). \n                      https:\/\/doi.org\/10.1109\/ICCV.2015.133","DOI":"10.1109\/ICCV.2015.133"},{"key":"23_CR22","doi-asserted-by":"publisher","unstructured":"Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: IEEE International Conference on Computer Vision, pp. 3774\u20133782 (2017). \n                      https:\/\/doi.org\/10.1109\/ICCV.2017.405","DOI":"10.1109\/ICCV.2017.405"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zheng, L., Yang, Y.: Pedestrian alignment network for large-scale person re-identification. IEEE Trans. Circuits Syst. Video Technol. (2018)","DOI":"10.1109\/TCSVT.2018.2873599"},{"key":"23_CR24","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. arXiv preprint \n                      arXiv:1708.04896\n                      \n                     (2017)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Image Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30508-6_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T19:20:30Z","timestamp":1567970430000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30508-6_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030305079","9783030305086"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30508-6_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}