{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:33:22Z","timestamp":1742913202622,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030317256"},{"type":"electronic","value":"9783030317263"}],"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\/978-3-030-31726-3_43","type":"book-chapter","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T00:05:31Z","timestamp":1572480331000},"page":"503-515","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Person ReID: Optimization of Domain Adaption Though Clothing Style Transfer Between Datasets"],"prefix":"10.1007","author":[{"given":"Haijian","family":"Wang","sequence":"first","affiliation":[]},{"given":"Meng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Linbin","family":"Ye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,31]]},"reference":[{"key":"43_CR1","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: ICCV 2017 (2017)","DOI":"10.1109\/ICCV.2017.405"},{"key":"43_CR2","doi-asserted-by":"crossref","unstructured":"Qian, X., Fu, Y., Wang, W., et al.: Pose-normalized image generation for person re-identification. arXiv preprint. arXiv:1712.02225 (2017)","DOI":"10.1007\/978-3-030-01240-3_40"},{"key":"43_CR3","doi-asserted-by":"crossref","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: ICCV 2015 (2015)","DOI":"10.1109\/ICCV.2015.133"},{"key":"43_CR4","doi-asserted-by":"crossref","unstructured":"Li, W., Zhao, R., Xiao, T., Wang, X.: Deepreid: deep filter pairing neural network for person re-identification. In: CVPR 2014 (2014)","DOI":"10.1109\/CVPR.2014.27"},{"key":"43_CR5","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y.K., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.304"},{"key":"43_CR6","unstructured":"Kim, T., Cha, M., Kim, H., Lee, J.K., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. arXiv preprint. arXiv:1703.05192 (2017)"},{"key":"43_CR7","unstructured":"Taigman, Y., Polyak, A., Wolf, L.: Unsupervised cross-domain image generation. arXiv preprint. arXiv:1611.02200 (2016)"},{"key":"43_CR8","unstructured":"Zheng, Z., Zheng, L., Yang, Y.: A discriminatively learned CNN embedding for person re-identification. In: TOMM (2016)"},{"key":"43_CR9","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhang, S., Gao, W., et al.: Person transfer GAN to bridge domain gap for person re-identification. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00016"},{"key":"43_CR10","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: NIPS 2014 (2014)"},{"key":"43_CR11","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. arXiv preprint. arXiv:1701.07717 (2017)","DOI":"10.1109\/ICCV.2017.405"},{"key":"43_CR12","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Zheng, Z., et al.: Camera style adaptation for person re-identification. arXiv preprint. arXiv:1711.10295 (2017)","DOI":"10.1109\/CVPR.2018.00541"},{"key":"43_CR13","unstructured":"Ge, Y., et al.: FD-GAN: pose-guided feature distilling GAN for robust person re-identification. In: CVPR (2018)"},{"key":"43_CR14","doi-asserted-by":"crossref","unstructured":"Song, C., Huang, Y., Ouyang, W., Wang, L.: Mask-guided contrastive attention model for person re-identification. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00129"},{"key":"43_CR15","doi-asserted-by":"crossref","unstructured":"Yang, W., Luo, P., Lin, L.: Clothing co-parsing by joint image segmentation and labeling. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.407"},{"issue":"12","key":"43_CR16","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1109\/TPAMI.2015.2408360","volume":"37","author":"X Liang","year":"2015","unstructured":"Liang, X., et al.: Deep human parsing with active template regression. TPAMI 37(12), 2402\u20132414 (2015)","journal-title":"TPAMI"},{"key":"43_CR17","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint. arXiv:1703.10593 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"43_CR18","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"43_CR19","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR (2016)"},{"key":"43_CR20","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial nets. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"43_CR21","unstructured":"Mo, S., Choy, M., Shin, J.: InstaGAN: instance-aware image-to-image translation. In: ICLR (2019)"},{"key":"43_CR22","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"43_CR23","doi-asserted-by":"crossref","unstructured":"Peng, P., Xiang, T., Wang, Y., et al.: Unsupervised cross-dataset transfer learning for person reidentification. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.146"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-31726-3_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:19:47Z","timestamp":1730333987000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-31726-3_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030317256","9783030317263"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-31726-3_43","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":"31 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"8 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv2019.com\/en\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"412","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"165","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}