{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:34:12Z","timestamp":1743125652656,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031490170"},{"type":"electronic","value":"9783031490187"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-49018-7_6","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:21Z","timestamp":1701039741000},"page":"76-89","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Vehicle Re-Identification Based on\u00a0Unsupervised Domain Adaptation by\u00a0Incremental Generation of\u00a0Pseudo-Labels"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1248-0083","authenticated-orcid":false,"given":"Paula","family":"Moral","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1705-3972","authenticated-orcid":false,"given":"\u00c1lvaro","family":"Garc\u00eda-Mart\u00edn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2236-1769","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"Mart\u00ednez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"6_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1007\/978-3-030-93420-0_24","volume-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications","author":"L Capozzi","year":"2021","unstructured":"Capozzi, L., Pinto, J.R., Cardoso, J.S., Rebelo, A.: Optimizing person re-identification using generated attention masks. In: Tavares, J.M.R.S., Papa, J.P., Gonz\u00e1lez Hidalgo, M. (eds.) CIARP 2021. LNCS, vol. 12702, pp. 248\u2013257. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-93420-0_24"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Cascante-Bonilla, P., Tan, F., Qi, Y., Ordonez, V.: Curriculum labeling: revisiting pseudo-labeling for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 6912\u20136920 (2021)","DOI":"10.1609\/aaai.v35i8.16852"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"6_CR4","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226\u2013231 (1996)"},{"issue":"4","key":"6_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3243316","volume":"14","author":"H Fan","year":"2018","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)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L.V.D., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"6_CR7","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.cviu.2019.03.001","volume":"182","author":"SD Khan","year":"2019","unstructured":"Khan, S.D., Ullah, H.: A survey of advances in vision-based vehicle re-identification. Comput. Vis. Image Underst. 182, 50\u201363 (2019). https:\/\/doi.org\/10.1016\/j.cviu.2019.03.001","journal-title":"Comput. Vis. Image Underst."},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, W., Ma, H., Fu, H.: Large-scale vehicle re-identification in urban surveillance videos. In: 2016 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/ICME.2016.7553002"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Luo, H., et al.: An empirical study of vehicle re-identification on the AI city challenge. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4095\u20134102 (2021)","DOI":"10.1109\/CVPRW53098.2021.00462"},{"issue":"24","key":"6_CR10","doi-asserted-by":"publisher","first-page":"36815","DOI":"10.1007\/s11042-023-14511-0","volume":"82","author":"P Moral","year":"2023","unstructured":"Moral, P., Garc\u00eda-Mart\u00edn, \u00c1., Mart\u00ednez, J.M., Besc\u00f3s, J.: Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information. Multimedia Tools Appl. 82(24), 36815\u201336835 (2023). https:\/\/doi.org\/10.1007\/s11042-023-14511-0","journal-title":"Multimedia Tools Appl."},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Naphade, M., et al.: The 5th AI city challenge. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4263\u20134273 (2021)","DOI":"10.1109\/CVPRW53098.2021.00482"},{"key":"6_CR12","unstructured":"Nowruzi, F.E., Kapoor, P., Kolhatkar, D., Hassanat, F.A., Laganiere, R., Rebut, J.: How much real data do we actually need: analyzing object detection performance using synthetic and real data. arXiv preprint arXiv:1907.07061 (2019)"},{"key":"6_CR13","doi-asserted-by":"publisher","unstructured":"Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X.: DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN. ACM Trans. Database Syst. 42(3), 1\u201321 (2017). https:\/\/doi.org\/10.1145\/3068335","DOI":"10.1145\/3068335"},{"key":"6_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107173","volume":"102","author":"L Song","year":"2020","unstructured":"Song, L., et al.: Unsupervised domain adaptive re-identification: theory and practice. Pattern Recogn. 102, 107173 (2020)","journal-title":"Pattern Recogn."},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Tang, Z., et al.: CityFlow: a city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8797\u20138806 (2019)","DOI":"10.1109\/CVPR.2019.00900"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Jain, H., Bucher, M., Cord, M., P\u00e9rez, P.: ADVENT: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"6_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1007\/978-3-030-58568-6_38","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Wang","year":"2020","unstructured":"Wang, H., Shen, T., Zhang, W., Duan, L.-Y., Mei, T.: Classes matter: a fine-grained adversarial approach to cross-domain semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 642\u2013659. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_38"},{"issue":"1","key":"6_CR18","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TPAMI.2019.2924417","volume":"43","author":"W Wang","year":"2019","unstructured":"Wang, W., Shen, J., Xie, J., Cheng, M.M., Ling, H., Borji, A.: Revisiting video saliency prediction in the deep learning era. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 220\u2013237 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Wu, J., Liao, S., Wang, X., Yang, Y., Li, S.Z., et al.: Clustering and dynamic sampling based unsupervised domain adaptation for person re-identification. In: 2019 IEEE International Conference on Multimedia and Expo, pp. 886\u2013891. IEEE (2019)","DOI":"10.1109\/ICME.2019.00157"},{"key":"6_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/978-3-030-58539-6_46","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Yao","year":"2020","unstructured":"Yao, Y., Zheng, L., Yang, X., Naphade, M., Gedeon, T.: Simulating content consistent vehicle datasets with attribute descent. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 775\u2013791. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58539-6_46"},{"issue":"2","key":"6_CR21","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11263-016-0907-4","volume":"120","author":"D Zhang","year":"2016","unstructured":"Zhang, D., Han, J., Li, C., Wang, J., Li, X.: Detection of co-salient objects by looking deep and wide. Int. J. Comput. Vision 120(2), 215\u2013232 (2016). https:\/\/doi.org\/10.1007\/s11263-016-0907-4","journal-title":"Int. J. Comput. Vision"},{"issue":"4","key":"6_CR22","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1109\/TITS.2011.2158001","volume":"12","author":"J Zhang","year":"2011","unstructured":"Zhang, J., Wang, F.Y., Wang, K., Lin, W.H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624\u20131639 (2011)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, M., et al.: Unsupervised domain adaptation for person re-identification via heterogeneous graph alignment. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3360\u20133368 (2021)","DOI":"10.1609\/aaai.v35i4.16448"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cao, J., Shen, C., You, M.: Self-training with progressive augmentation for unsupervised cross-domain person re-identification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8222\u20138231 (2019)","DOI":"10.1109\/ICCV.2019.00831"},{"issue":"3","key":"6_CR25","first-page":"1","volume":"5","author":"Y Zheng","year":"2014","unstructured":"Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5(3), 1\u201355 (2014)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., Kautz, J.: Joint discriminative and generative learning for person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2138\u20132147 (2019)","DOI":"10.1109\/CVPR.2019.00224"},{"key":"6_CR27","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 598\u2013607 (2019)","DOI":"10.1109\/CVPR.2019.00069"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49018-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:09:16Z","timestamp":1701040156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49018-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9783031490170","9783031490187"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49018-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Coimbra","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2023","order":10,"name":"conference_id","label":"Conference ID","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":"Conftool","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"106","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":"61","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":"58% - 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":"2","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":"5","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)"}}]}}