{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:59:25Z","timestamp":1777510765982,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819970186","type":"print"},{"value":"9789819970193","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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\/978-981-99-7019-3_33","type":"book-chapter","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:02:57Z","timestamp":1699574577000},"page":"353-364","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["DSAM-GN: Graph Network Based on\u00a0Dynamic Similarity Adjacency Matrices for\u00a0Vehicle Re-identification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7224-0192","authenticated-orcid":false,"given":"Yuejun","family":"Jiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingsong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingding","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingli","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 549\u2013556 (2020)","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M.: Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 27\u201334 (2020)","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"33_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/978-3-030-01246-5_40","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Guo","year":"2018","unstructured":"Guo, M., Chou, E., Huang, D.-A., Song, S., Yeung, S., Fei-Fei, L.: Neural graph matching networks for Fewshot 3D action recognition. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 673\u2013689. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_40"},{"issue":"9","key":"33_CR4","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.3390\/electronics11091354","volume":"11","author":"W Huang","year":"2022","unstructured":"Huang, W., et al.: Vehicle re-identification with spatio-temporal model leveraging by pose view embedding. Electronics 11(9), 1354 (2022)","journal-title":"Electronics"},{"key":"33_CR5","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"issue":"5","key":"33_CR6","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1109\/TETCI.2021.3127906","volume":"6","author":"H Li","year":"2022","unstructured":"Li, H., et al.: Attributes guided feature learning for vehicle re-identification. IEEE Trans. Emerg. Top. Comput. Intell. 6(5), 1211\u20131221 (2022)","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Liu, H., Tian, Y., Wang, Y., Pang, L., Huang, T.: Deep relative distance learning: tell the difference between similar vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2167\u20132175 (2016)","DOI":"10.1109\/CVPR.2016.238"},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Liu, H., Tian, Y., Yang, Y., Pang, L., Huang, T.: Deep relative distance learning: tell the difference between similar vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2167\u20132175 (2016)","DOI":"10.1109\/CVPR.2016.238"},{"key":"33_CR9","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"},{"issue":"3","key":"33_CR10","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TMM.2017.2751966","volume":"20","author":"X Liu","year":"2017","unstructured":"Liu, X., Liu, W., Mei, T., Ma, H.: PROVID: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans. Multimedia 20(3), 645\u2013658 (2017)","journal-title":"IEEE Trans. Multimedia"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, W., Zheng, J., Yan, C., Mei, T.: Beyond the parts: learning multi-view cross-part correlation for vehicle re-identification. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 907\u2013915 (2020)","DOI":"10.1145\/3394171.3413578"},{"issue":"11","key":"33_CR12","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Meng, D., et al.: Parsing-based view-aware embedding network for vehicle re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7103\u20137112 (2020)","DOI":"10.1109\/CVPR42600.2020.00713"},{"issue":"4","key":"33_CR14","doi-asserted-by":"publisher","first-page":"594","DOI":"10.3390\/e25040594","volume":"25","author":"X Pang","year":"2023","unstructured":"Pang, X., Yin, Y., Zheng, Y.: Multi-receptive field soft attention part learning for vehicle re-identification. Entropy 25(4), 594 (2023)","journal-title":"Entropy"},{"issue":"9","key":"33_CR15","doi-asserted-by":"publisher","first-page":"095401","DOI":"10.1088\/1361-6501\/ab8b81","volume":"31","author":"J Qian","year":"2020","unstructured":"Qian, J., Jiang, W., Luo, H., Yu, H.: Stripe-based and attribute-aware network: a two-branch deep model for vehicle re-identification. Meas. Sci. Technol. 31(9), 095401 (2020)","journal-title":"Meas. Sci. Technol."},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"issue":"5","key":"33_CR17","doi-asserted-by":"publisher","first-page":"4005","DOI":"10.1007\/s40747-022-00692-y","volume":"8","author":"J Shen","year":"2022","unstructured":"Shen, J., Sun, J., Wang, X., Mao, Z.: Joint metric learning of local and global features for vehicle re-identification. Complex Intell. Syst. 8(5), 4005\u20134020 (2022)","journal-title":"Complex Intell. Syst."},{"key":"33_CR18","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.neucom.2021.07.082","volume":"463","author":"AMN Taufique","year":"2021","unstructured":"Taufique, A.M.N., Savakis, A.: LABNet: local graph aggregation network with class balanced loss for vehicle re-identification. Neurocomputing 463, 122\u2013132 (2021)","journal-title":"Neurocomputing"},{"key":"33_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"20","key":"33_CR20","first-page":"10","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. STAT 1050(20), 10\u201348550 (2017)","journal-title":"STAT"},{"key":"33_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/978-3-030-68821-9_32","volume-title":"Pattern Recognition. ICPR International Workshops and Challenges","author":"Z Xu","year":"2021","unstructured":"Xu, Z., Wei, L., Lang, C., Feng, S., Wang, T., Bors, A.G.: HSS-GCN: a hierarchical spatial structural graph convolutional network for vehicle re-identification. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12665, pp. 356\u2013364. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68821-9_32"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Yu, Z., Huang, Z., Pei, J., Tahsin, L., Sun, D.: Semantic-oriented feature coupling transformer for vehicle re-identification in intelligent transportation system. IEEE Trans. Intell. Transp. Syst., 1\u201311 (2023)","DOI":"10.1109\/TITS.2023.3257873"},{"issue":"13","key":"33_CR23","doi-asserted-by":"publisher","first-page":"14799","DOI":"10.1007\/s10489-022-03349-y","volume":"52","author":"C Zhang","year":"2022","unstructured":"Zhang, C., Yang, C., Wu, D., Dong, H., Deng, B.: Cross-view vehicle re-identification based on graph matching. Appl. Intell. 52(13), 14799\u201314810 (2022)","journal-title":"Appl. Intell."},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zha, Z.J., Zhang, T., Liu, J., Luo, J.: A structured graph attention network for vehicle re-identification. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 646\u2013654 (2020)","DOI":"10.1145\/3394171.3413607"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2023: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7019-3_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:18:01Z","timestamp":1699575481000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7019-3_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"ISBN":["9789819970186","9789819970193"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7019-3_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"10 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jakarta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","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":"15 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2023\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"422","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":"95","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":"36","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":"23% - 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":"3.4","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":"3.1","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)"}}]}}