{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:28:08Z","timestamp":1774121288543,"version":"3.50.1"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030208899","type":"print"},{"value":"9783030208905","type":"electronic"}],"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\/978-3-030-20890-5_2","type":"book-chapter","created":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T11:18:34Z","timestamp":1559387914000},"page":"19-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-identification"],"prefix":"10.1007","author":[{"given":"Xing","family":"Fan","sequence":"first","affiliation":[]},{"given":"Hao","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lingxiao","family":"He","sequence":"additional","affiliation":[]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,2]]},"reference":[{"key":"2_CR1","unstructured":"Almazan, J., Gajic, B., Murray, N., Larlus, D.: Re-id done right: towards good practices for person re-identification. arXiv preprint \n                      arXiv:1801.05339\n                      \n                     (2018)"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Bai, S., Bai, X., Tian, Q.: Scalable person re-identification on supervised smoothed manifold. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.358"},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.cviu.2017.12.002","volume":"167","author":"IB Barbosa","year":"2018","unstructured":"Barbosa, I.B., Cristani, M., Caputo, B., Rognhaugen, A., Theoharis, T.: Looking beyond appearances: synthetic training data for deep CNNs in re-identification. Comput. Vis. Image Underst. 167, 50\u201362 (2018)","journal-title":"Comput. Vis. Image Underst."},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.145"},{"issue":"2","key":"2_CR5","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1109\/TPAMI.2017.2666805","volume":"40","author":"YC Chen","year":"2018","unstructured":"Chen, Y.C., Zhu, X., Zheng, W.S., Lai, J.H.: Person re-identification by camera correlation aware feature augmentation. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 392\u2013408 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.149"},{"key":"2_CR7","unstructured":"Donahue, J., et al.: Decaf: a deep convolutional activation feature for generic visual recognition. In: ICML (2014)"},{"key":"2_CR8","unstructured":"Fan, X., Jiang, W., Luo, H., Fei, M.: SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification. arXiv preprint \n                      arXiv: 1807.00537\n                      \n                     (2018)"},{"issue":"9","key":"2_CR9","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627\u20131645 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR10","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":"2_CR11","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"He, L., Liang, J., Li, H., Sun, Z.: Deep spatial feature reconstruction for partial person re-identification: Alignment-free approach. arXiv preprint \n                      arXiv:1801.00881\n                      \n                     (2018)","DOI":"10.1109\/CVPR.2018.00739"},{"key":"2_CR14","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":"2_CR15","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)","DOI":"10.1109\/CVPR.2014.27"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Li, W., Zhu, X., Gong, S.: Harmonious attention network for person re-identification. arXiv preprint \n                      arXiv:1802.08122\n                      \n                     (2018)","DOI":"10.1109\/CVPR.2018.00243"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298832"},{"key":"2_CR18","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":"2_CR19","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: Neural person search machines. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.61"},{"key":"2_CR20","doi-asserted-by":"publisher","first-page":"3492","DOI":"10.1109\/TIP.2017.2700762","volume":"26","author":"H Liu","year":"2017","unstructured":"Liu, H., Feng, J., Qi, M., Jiang, J., Yan, S.: End-to-end comparative attention networks for person re-identification. IEEE Trans. Image Process. 26, 3492\u20133506 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR21","unstructured":"Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. In: NIPS (2016)"},{"key":"2_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-319-48881-3_2","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"E Ristani","year":"2016","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17\u201335. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-48881-3_2"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Schumann, A., Gong, S., Schuchert, T.: Deep learning prototype domains for person re-identification. arXiv preprint \n                      arXiv:1610.05047\n                      \n                     (2016)","DOI":"10.1109\/ICIP.2017.8296585"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, L., Deng, W., Wang, S.: SVDNet for pedestrian retrieval. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.410"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling. arXiv preprint \n                      arXiv:1711.09349\n                      \n                     (2017)","DOI":"10.1007\/978-3-030-01225-0_30"},{"key":"2_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1007\/978-3-319-46484-8_48","volume-title":"Computer Vision \u2013 ECCV 2016","author":"RR Varior","year":"2016","unstructured":"Varior, R.R., Haloi, M., Wang, G.: Gated siamese convolutional neural network architecture for human re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 791\u2013808. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-46484-8_48"},{"key":"2_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-3-319-46478-7_9","volume-title":"Computer Vision \u2013 ECCV 2016","author":"RR Varior","year":"2016","unstructured":"Varior, R.R., Shuai, B., Lu, J., Xu, D., Wang, G.: A Siamese long short-term memory architecture for human re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 135\u2013153. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-46478-7_9"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhang, S., Yao, H., Gao, W., Tian, Q.: GLAD: Global-local-alignment descriptor for pedestrian retrieval. In: ACM Multimedia (2017)","DOI":"10.1145\/3123266.3123279"},{"key":"2_CR29","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":"2_CR30","unstructured":"Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: End-to-end deep learning for person search. arXiv preprint \n                      arXiv:1604.01850\n                      \n                     (2016)"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.360"},{"key":"2_CR32","unstructured":"Yao, H., Zhang, S., Zhang, Y., Li, J., Tian, Q.: Deep representation learning with part loss for person re-identification. arXiv preprint \n                      arXiv:1707.00798\n                      \n                     (2017)"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xiang, T., Gong, S.: Learning a discriminative null space for person re-identification. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.139"},{"key":"2_CR34","unstructured":"Zhang, X., et al.: AlignedReID: surpassing human-level performance in person re-identification. arXiv preprint \n                      arXiv: 1711.08184\n                      \n                     (2017)"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Zhao, H., et al.: Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.103"},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Zhao, L., Li, X., Zhuang, Y., Wang, J.: Deeply-learned part-aligned representations for person re-identification. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.349"},{"key":"2_CR37","unstructured":"Zheng, L., Huang, Y., Lu, H., Yang, Y.: Pose invariant embedding for deep person re-identification. arXiv preprint \n                      arXiv:1701.07732\n                      \n                     (2017)"},{"key":"2_CR38","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)","DOI":"10.1109\/ICCV.2015.133"},{"key":"2_CR39","unstructured":"Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future. arXiv preprint \n                      arXiv:1610.02984\n                      \n                     (2016)"},{"key":"2_CR40","doi-asserted-by":"crossref","unstructured":"Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: CVPR (2011)","DOI":"10.1109\/CVPR.2011.5995598"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Zheng, W.S., Li, X., Xiang, T., Liao, S., Lai, J., Gong, S.: Partial person re-identification. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.531"},{"key":"2_CR42","unstructured":"Zheng, Z., Zheng, L., Yang, Y.: A discriminatively learned CNN embedding for person re-identification. arXiv preprint \n                      arXiv:1611.05666\n                      \n                     (2016)"},{"key":"2_CR43","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.389"},{"key":"2_CR44","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Zheng, Z., Li, S., Yang, Y.: Camera style adaptation for person re-identification. arXiv preprint \n                      arXiv:1711.10295\n                      \n                     (2017)","DOI":"10.1109\/CVPR.2018.00541"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2018"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20890-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T11:21:52Z","timestamp":1559388112000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20890-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030208899","9783030208905"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20890-5_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"2 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Perth, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2018.net\/","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"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"979","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"274","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"28% - 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"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}