{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:14:18Z","timestamp":1742998458892,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819985647"},{"type":"electronic","value":"9789819985654"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-8565-4_24","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T06:03:13Z","timestamp":1701410593000},"page":"249-258","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Few-Shot Person Re-identification Based on\u00a0Hybrid Pooling Fusion and\u00a0Gaussian Relation Metric"],"prefix":"10.1007","author":[{"given":"Guizhen","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guofeng","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinjie","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"issue":"17","key":"24_CR1","doi-asserted-by":"publisher","first-page":"26855","DOI":"10.1007\/s11042-021-10953-6","volume":"80","author":"G Zou","year":"2021","unstructured":"Zou, G., Fu, G., Peng, X., Liu, Y., Gao, M., Liu, Z.: Person re-identification based on metric learning: a survey. Multimed. Tools Appl. 80(17), 26855\u201326888 (2021)","journal-title":"Multimed. Tools Appl."},{"key":"24_CR2","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.neunet.2023.01.033","volume":"161","author":"Z Liu","year":"2023","unstructured":"Liu, Z., Feng, C., Chen, S., Hu, J.: Knowledge-preserving continual person re-identification using graph attention network. Neural Netw. 161, 105\u2013115 (2023)","journal-title":"Neural Netw."},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Song, Y., Wang, T., Cai, P., Mondal, S.K., Sahoo, J.P.: A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. ACM Comput. Surv. (2023)","DOI":"10.1145\/3582688"},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Lv, J., Chen, W., Li, Q., Yang, C.: Unsupervised cross-dataset person re-identification by transfer learning of spatial-temporal patterns. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7948\u20137956 (2018)","DOI":"10.1109\/CVPR.2018.00829"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Ding, G., Khan, S., Tang, Z., Zhang, J., Porikli, F.: Towards better validity: dispersion based clustering for unsupervised person re-identification. arXiv preprint arXiv:1906.01308 (2019)","DOI":"10.1109\/TMM.2019.2916456"},{"key":"24_CR6","unstructured":"Mehrotra, A., Dukkipati, A.: Generative adversarial residual pairwise networks for one shot learning. arXiv preprint arXiv:1703.08033 (2017)"},{"key":"24_CR7","unstructured":"Schwartz, E., et al.: Delta-encoder: an effective sample synthesis method for few-shot object recognition. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"24_CR8","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"24_CR9","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"24_CR10","unstructured":"F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208 (2018)"},{"key":"24_CR11","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135. PMLR (2017)"},{"key":"24_CR12","unstructured":"Lu, Y., Wang, Y., Wang, W.: Transformer-based few-shot and fine-grained image classification method. Comput. Eng. Appl. 1\u201311 (2022)"},{"issue":"12","key":"24_CR13","first-page":"220","volume":"42","author":"H Meng","year":"2021","unstructured":"Meng, H., Tian, Y., Sun, Y., Li, T.: Few shot ship recognition based on universal attention relationnet. Chin. J. Sci. Instrum. 42(12), 220\u2013227 (2021)","journal-title":"Chin. J. Sci. Instrum."},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Wertheimer, D., Tang, L., Hariharan, B.: Few-shot classification with feature map reconstruction networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8012\u20138021 (2021)","DOI":"10.1109\/CVPR46437.2021.00792"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Lee, S., Moon, W., Heo, J.-P.: Task discrepancy maximization for fine-grained few-shot classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5331\u20135340 (2022)","DOI":"10.1109\/CVPR52688.2022.00526"},{"key":"24_CR16","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1109\/TIP.2020.3043128","volume":"30","author":"X Li","year":"2020","unstructured":"Li, X., Wu, J., Sun, Z., Ma, Z., Cao, J., Xue, J.-H.: BSNet: bi-similarity network for few-shot fine-grained image classification. IEEE Trans. Image Process. 30, 1318\u20131331 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Xie, J., Long, F., Lv, J., Wang, Q., Li, P.: Joint distribution matters: deep Brownian distance covariance for few-shot classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7972\u20137981 (2022)","DOI":"10.1109\/CVPR52688.2022.00781"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Afrasiyabi, A., Larochelle, H., Lalonde, J.-F., Gagn\u00e9, C.: Matching feature sets for few-shot image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9014\u20139024 (2022)","DOI":"10.1109\/CVPR52688.2022.00881"}],"container-title":["Lecture Notes in Computer Science","Biometric Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8565-4_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T00:11:31Z","timestamp":1701735091000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8565-4_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819985647","9789819985654"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8565-4_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"2 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Biometric Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xuzhou","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccbr2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ccbr99.cn\/","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":"79","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":"41","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":"52% - 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":"3","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)"}}]}}