{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:45:57Z","timestamp":1772905557195,"version":"3.50.1"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585822","type":"print"},{"value":"9783030585839","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58583-9_10","type":"book-chapter","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T10:08:18Z","timestamp":1605694098000},"page":"159-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":92,"title":["Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification"],"prefix":"10.1007","author":[{"given":"Djebril","family":"Mekhazni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amran","family":"Bhuiyan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Ekladious","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Granger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Bhuiyan, A., Liu, Y., Siva, P., Javan, M., Ayed, I.B., Granger, E.: Pose guided gated fusion for person re-identification. In: WACV (2020)","DOI":"10.1109\/WACV45572.2020.9093370"},{"key":"10_CR2","unstructured":"Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: Domain separation networks. In: NIPS (2016)"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Chang, X., Yang, Y., Xiang, T., Hospedales, T.M.: Disjoint label space transfer learning with common factorised space. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33013288"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Chen, B., Deng, W., Hu, J.: Mixed high-order attention network for person re-identification. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00046"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., Jiao, J.: Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00110"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Ekladious, G., Lemoine, H., Granger, E., Kamali, K., Moudache, S.: Dual-triplet metric learning for unsupervised domain adaptation in video-based face recognition. In: IJCNN (2020)","DOI":"10.1109\/IJCNN48605.2020.9206794"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Fan, H., Zheng, L., Yan, C., Yang, Y.: Unsupervised person re-identification: clustering and fine-tuning. ACM Trans. Multimed. Comput. Commun. Appl. 14(4), 1\u201318 (2018)","DOI":"10.1145\/3243316"},{"issue":"9","key":"10_CR8","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2009","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 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10_CR9","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M.J., Sch\u00f6lkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13(1), 723\u2013773 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR10","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification (2017)"},{"key":"10_CR11","unstructured":"Li, C.L., Chang, W.C., Cheng, Y., Yang, Y., P\u00f3czos, B.: MMD gan: towards deeper understanding of moment matching network. In: NIPS (2017)"},{"key":"10_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1007\/978-3-030-01225-0_45","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Li","year":"2018","unstructured":"Li, M., Zhu, X., Gong, S.: Unsupervised person re-identification by deep learning tracklet association. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 772\u2013788. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_45"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Li, M., Zhu, X., Gong, S.: Unsupervised tracklet person re-identification. IEEE TPAMI (2019)","DOI":"10.1109\/TPAMI.2019.2903058"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Li, Y.J., Lin, C.S., Lin, Y.B., Wang, Y.C.F.: Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00801"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Li, Y.J., Yang, F.E., Liu, Y.C., Yeh, Y.Y., Du, X., Frank Wang, Y.C.: Adaptation and re-identification network: an unsupervised deep transfer learning approach to person re-identification. In: CVPR Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00054"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Lin, Y., Dong, X., Zheng, L., Yan, Y., Yang, Y.: A bottom-up clustering approach to unsupervised person re-identification. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33018738"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: CVPR Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Nguyen-Meidine, L.T., Granger, E., Kiran, M., Dolz, J., Blais-Morin, L.A.: Joint progressive knowledge distillation and unsupervised domain adaptation. In: IJCNN (2020)","DOI":"10.1109\/IJCNN48605.2020.9206989"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Qi, L., Wang, L., Huo, J., Zhou, L., Shi, Y., Gao, Y.: A novel unsupervised camera-aware domain adaptation framework for person re-identification. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00817"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Quan, R., Dong, X., Wu, Y., Zhu, L., Yang, Y.: Auto-reID: searching for a part-aware convnet for person re-identification. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00385"},{"key":"10_CR21","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\u00a0multi-target, multi-camera tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17\u201335. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_2"},{"key":"10_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"B Sun","year":"2016","unstructured":"Sun, B., Saenko, K.: Deep CORAL: Correlation Alignment for Deep Domain Adaptation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443\u2013450. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_35"},{"key":"10_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-030-01225-0_30","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y 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). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501\u2013518. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_30"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Wang, G., Lai, J., Huang, P., Xie, X.: Spatial-temporal person re-identification. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33018933"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhu, X., Gong, S., Li, W.: Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00242"},{"key":"10_CR28","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","volume":"312","author":"M Wang","year":"2018","unstructured":"Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135\u2013153 (2018)","journal-title":"Neurocomputing"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer gan to bridge domain gap for person re-identification. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00016"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Wu, A., Zheng, W.S., Lai, J.H.: Unsupervised person re-identification by camera-aware similarity consistency learning. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00702"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., Zuo, W.: Mind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.107"},{"key":"10_CR32","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":"10_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/978-3-030-01261-8_11","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Z Zhong","year":"2018","unstructured":"Zhong, Z., Zheng, L., Li, S., Yang, Y.: Generalizing a person retrieval model hetero- and homogeneously. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 176\u2013192. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_11"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Luo, Z., Li, S., Yang, Y.: Invariance matters: exemplar memory for domain adaptive person re-identification. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00069"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58583-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:05:03Z","timestamp":1731888303000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58583-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585822","9783030585839"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58583-9_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"3","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":"7","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}