{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:51:17Z","timestamp":1742914277292,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819985395"},{"type":"electronic","value":"9789819985401"}],"license":[{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"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\/978-981-99-8540-1_37","type":"book-chapter","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T18:01:32Z","timestamp":1703440892000},"page":"462-474","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Penalty-Aware Memory Loss for\u00a0Deep Metric Learning"],"prefix":"10.1007","author":[{"given":"Qian","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Run","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianming","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,25]]},"reference":[{"key":"37_CR1","doi-asserted-by":"crossref","unstructured":"Artsiom, S., Vadim, T., Uta, B., Bjorn, O.: Divide and conquer the embedding space for metric learning. In: CVPR, pp. 471\u2013480 (2019)","DOI":"10.1109\/CVPR.2019.00056"},{"key":"37_CR2","unstructured":"Ben, H., BG, K., Gustavo, C., Ian, R., Tom, D., et al.: Smart mining for deep metric learning. In: ICCV, pp. 2821\u20132829 (2017)"},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Stefanos, Z.: Arcface: additive angular margin loss for deep face recognition. In: CVPR, pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Duan, Y., Zheng, W., Lin, X., Lu, J., Zhou, J.: Deep adversarial metric learning. In: CVPR, pp. 2780\u20132789 (2018)","DOI":"10.1109\/CVPR.2018.00294"},{"key":"37_CR5","unstructured":"Fatih, C., He, K., Xia, X., Brian, K., Stan, S.: Deep metric learning to rank. In: CVPR, pp. 1861\u20131870 (2019)"},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Feng, J., Wu, A., Zheng, W.S.: Shape-erased feature learning for visible-infrared person re-identification. In: CVPR, pp. 22752\u201322761 (2023)","DOI":"10.1109\/CVPR52729.2023.02179"},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Florian, S., Dmitry, K., James, P.: Facenet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Ge, W.: Deep metric learning with hierarchical triplet loss. In: ECCV, pp. 269\u2013285 (2018)","DOI":"10.1007\/978-3-030-01231-1_17"},{"key":"37_CR9","unstructured":"Hong, X., Richard, S., Robert, P.: Deep randomized ensembles for metric learning. In: ECCV, pp. 723\u2013734 (2018)"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Hyun, O., Xiang, Y., Stefanie, J., Silvio, S.: Deep metric learning via lifted structured feature embedding. In: CVPR, pp. 4004\u20134012 (2016)","DOI":"10.1109\/CVPR.2016.434"},{"key":"37_CR11","unstructured":"Jonathan, K., Michael, S., Deng, J., Li, F.: 3D object representations for fine-grained categorization. In: ICCV Workshops, pp. 554\u2013561 (2013)"},{"key":"37_CR12","unstructured":"Karsten, R., Biagio, B., Bjorn, O.: MIC: mining interclass characteristics for improved metric learning. In: ICCV, pp. 8000\u20138009 (2019)"},{"key":"37_CR13","unstructured":"Kihyuk, S.: Improved deep metric learning with multi-class n-pair loss objective. In: NeurIPS, pp. 1857\u20131865 (2016)"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Kim, M., Jain, A.K., Liu, X.: Adaface: quality adaptive margin for face recognition. In: CVPR, pp. 18750\u201318759 (2022)","DOI":"10.1109\/CVPR52688.2022.01819"},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"Kim, M., Liu, F., Jain, A., Liu, X.: DCFace: synthetic face generation with dual condition diffusion model. In: CVPR, pp. 12715\u201312725 (2023)","DOI":"10.1109\/CVPR52729.2023.01223"},{"issue":"93","key":"37_CR16","first-page":"3221","volume":"15","author":"V Laurens","year":"2014","unstructured":"Laurens, V.: Accelerating t-SNE using tree-based algorithms. JMLR 15(93), 3221\u20133245 (2014)","journal-title":"JMLR"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Lin, X., et al.: Learning modal-invariant and temporal-memory for video-based visible-infrared person re-identification. In: CVPR, pp. 20973\u201320982 (2022)","DOI":"10.1109\/CVPR52688.2022.02030"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: Deepfashion: powering robust clothes recognition and retrieval with rich annotations. In: CVPR, pp. 1096\u20131104 (2016)","DOI":"10.1109\/CVPR.2016.124"},{"key":"37_CR19","unstructured":"Michael, O., Georg, W., Horst, P., Horst, B.: Bier-boosting independent embeddings robustly. In: ICCV, pp. 5189\u20135198 (2017)"},{"issue":"2","key":"37_CR20","first-page":"276","volume":"42","author":"O Michael","year":"2018","unstructured":"Michael, O., Georg, W., Horst, P., Horst, B.: Deep metric learning with bier: Boosting independent embeddings robustly. IEEE TPAMI 42(2), 276\u2013290 (2018)","journal-title":"IEEE TPAMI"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Shen, Y., Sun, X., Wei, X.S.: Equiangular basis vectors. In: CVPR, pp. 11755\u201311765 (2023)","DOI":"10.1109\/CVPR52729.2023.01131"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Sumit, C., Raia, H., Yann, L., et al.: Learning a similarity metric discriminatively, with application to face verification. In: CVPR, vol. 1, pp. 539\u2013546 (2005)","DOI":"10.1109\/CVPR.2005.202"},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Sun, Y., et al.: Circle loss: a unified perspective of pair similarity optimization. In: CVPR, pp. 6398\u20136407 (2020)","DOI":"10.1109\/CVPR42600.2020.00643"},{"key":"37_CR24","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD birds-200-2011 dataset (2011)"},{"key":"37_CR25","doi-asserted-by":"crossref","unstructured":"Wang, C., Zheng, W., Li, J., Zhou, J., Lu, J.: Deep factorized metric learning. In: CVPR, pp. 7672\u20137682 (2023)","DOI":"10.1109\/CVPR52729.2023.00741"},{"key":"37_CR26","doi-asserted-by":"crossref","unstructured":"Wang, F., Xiang, X., Cheng, J., Yuille, A.L.: Normface: L2 hypersphere embedding for face verification. In: ACM MM, pp. 1041\u20131049 (2017)","DOI":"10.1145\/3123266.3123359"},{"key":"37_CR27","unstructured":"Wonsik, K., Bhavya, G., Kunal, C., Jungmin, L., Keunjoo, K.: Attention-based ensemble for deep metric learning. In: ECCV, pp. 736\u2013751 (2018)"},{"key":"37_CR28","doi-asserted-by":"crossref","unstructured":"Xiong, J., Lai, J.: Similarity metric learning for RGB-infrared group re-identification. In: CVPR, pp. 13662\u201313671 (2023)","DOI":"10.1109\/CVPR52729.2023.01313"},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Yair, M., Alexander, T., K, L.T., Sergey, I., Saurabh, S.: No fuss distance metric learning using proxies. In: ICCV, pp. 360\u2013368 (2017)","DOI":"10.1109\/ICCV.2017.47"},{"key":"37_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108546","volume":"126","author":"H Yang","year":"2022","unstructured":"Yang, H., Chu, X., Zhang, L., Sun, Y., Li, D., Maybank, S.J.: Quadnet: quadruplet loss for multi-view learning in baggage re-identification. Pattern Recogn. 126, 108546 (2022)","journal-title":"Pattern Recogn."},{"key":"37_CR31","doi-asserted-by":"crossref","unstructured":"Yang, M., Huang, Z., Hu, P., Li, T., Lv, J., Peng, X.: Learning with twin noisy labels for visible-infrared person re-identification. In: CVPR, pp. 14308\u201314317 (2022)","DOI":"10.1109\/CVPR52688.2022.01391"},{"key":"37_CR32","doi-asserted-by":"crossref","unstructured":"Yu, B., Tao, D.: Deep metric learning with tuplet margin loss. In: ICCV, pp. 6490\u20136499 (2019)","DOI":"10.1109\/ICCV.2019.00659"},{"key":"37_CR33","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Yang, K., Zhang, C.: Hard-aware deeply cascaded embedding. In: ICCV, pp. 814\u2013823 (2017)","DOI":"10.1109\/ICCV.2017.94"},{"key":"37_CR34","doi-asserted-by":"crossref","unstructured":"Zheng, W., Chen, Z., Lu, J., Zhou, J.: Hardness-aware deep metric learning. In: CVPR, pp. 72\u201381 (2019)","DOI":"10.1109\/CVPR.2019.00016"},{"key":"37_CR35","doi-asserted-by":"crossref","unstructured":"Zheng, X., Ji, R., Sun, X., Y. Wu, Y.Y., Huang, F.: Towards optimal fine grained retrieval via decorrelated centralized loss with normalize-scale layer. In: AAAI, vol. 33, pp. 9291\u20139298 (2019)","DOI":"10.1609\/aaai.v33i01.33019291"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8540-1_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T20:07:20Z","timestamp":1730923640000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8540-1_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,25]]},"ISBN":["9789819985395","9789819985401"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8540-1_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,25]]},"assertion":[{"value":"25 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","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":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","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":"532","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":"37% - 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":"3,78","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":"3,69","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)"}}]}}