{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:02:02Z","timestamp":1742918522931,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442001"},{"type":"electronic","value":"9783031442018"}],"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\/978-3-031-44201-8_2","type":"book-chapter","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T08:03:20Z","timestamp":1695369800000},"page":"13-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Membership-Grade Based Prototype Rectification for\u00a0Fine-Grained Few-Shot Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2201-0437","authenticated-orcid":false,"given":"Sa","family":"Ning","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5880-1587","authenticated-orcid":false,"given":"Rundong","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,23]]},"reference":[{"key":"2_CR1","unstructured":"Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C.F., Huang, J.B.: A closer look at few-shot classification. arXiv preprint arXiv:1904.04232 (2019)"},{"key":"2_CR2","unstructured":"Chen, Y., Wang, X., Liu, Z., Xu, H., Darrell, T., et al.: A new meta-baseline for few-shot learning. arXiv preprint arXiv:2003.04390 2(3), 5 (2020)"},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.1109\/TIP.2021.3055617","volume":"30","author":"Y Ding","year":"2021","unstructured":"Ding, Y., et al.: AP-CNN: weakly supervised attention pyramid convolutional neural network for fine-grained visual classification. IEEE Trans. Image Process. 30, 2826\u20132836 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR4","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":"2_CR5","doi-asserted-by":"crossref","unstructured":"Fu, Y., Fu, Y., Jiang, Y.G.: Meta-FDMixup: cross-domain few-shot learning guided by labeled target data. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 5326\u20135334 (2021)","DOI":"10.1145\/3474085.3475655"},{"issue":"23","key":"2_CR6","doi-asserted-by":"publisher","first-page":"2987","DOI":"10.3390\/electronics10232987","volume":"10","author":"J Guo","year":"2021","unstructured":"Guo, J., Qi, G., Xie, S., Li, X.: Two-branch attention learning for fine-grained class incremental learning. Electronics 10(23), 2987 (2021)","journal-title":"Electronics"},{"key":"2_CR7","unstructured":"Hou, R., Chang, H., Ma, B., Shan, S., Chen, X.: Cross attention network for few-shot classification. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"issue":"2","key":"2_CR8","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1109\/TCSVT.2021.3065693","volume":"32","author":"H Huang","year":"2021","unstructured":"Huang, H., Zhang, J., Yu, L., Zhang, J., Wu, Q., Xu, C.: TOAN: target-oriented alignment network for fine-grained image categorization with few labeled samples. IEEE Trans. Circuits Syst. Video Technol. 32(2), 853\u2013866 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"2_CR9","unstructured":"Khosla, A., Jayadevaprakash, N., Yao, B., Li, F.F.: Novel dataset for fine-grained image categorization: Stanford dogs. In: Proceedings of the CVPR Workshop on Fine-Grained Visual Categorization (FGVC), vol. 2. Citeseer (2011)"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 554\u2013561 (2013)","DOI":"10.1109\/ICCVW.2013.77"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10657\u201310665 (2019)","DOI":"10.1109\/CVPR.2019.01091"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Li, W., Wang, L., Xu, J., Huo, J., Gao, Y., Luo, J.: Revisiting local descriptor based image-to-class measure for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7260\u20137268 (2019)","DOI":"10.1109\/CVPR.2019.00743"},{"key":"2_CR13","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":"2_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1007\/978-3-030-58452-8_43","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Liu","year":"2020","unstructured":"Liu, J., Song, L., Qin, Y.: Prototype rectification for few-shot learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part I. LNCS, vol. 12346, pp. 741\u2013756. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_43"},{"key":"2_CR15","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-031-19800-7_43","volume-title":"ECCV 2022, Part XIX","author":"Y Lu","year":"2022","unstructured":"Lu, Y., Wen, L., Liu, J., Liu, Y., Tian, X.: Self-supervision can be a good few-shot learner. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XIX. LNCS, vol. 13679, pp. 740\u2013758. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19800-7_43 Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings"},{"key":"2_CR16","unstructured":"Maniparambil, M., McGuinness, K., O\u2019Connor, N.: BaseTransformers: attention over base data-points for one shot learning. arXiv preprint arXiv:2210.02476 (2022)"},{"key":"2_CR17","unstructured":"Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676 (2018)"},{"key":"2_CR18","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Sung, 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)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"2_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108792","volume":"130","author":"H Tang","year":"2022","unstructured":"Tang, H., Yuan, C., Li, Z., Tang, J.: Learning attention-guided pyramidal features for few-shot fine-grained recognition. Pattern Recogn. 130, 108792 (2022)","journal-title":"Pattern Recogn."},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Tang, S., Chen, D., Bai, L., Liu, K., Ge, Y., Ouyang, W.: Mutual CRF-GNN for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2329\u20132339 (2021)","DOI":"10.1109\/CVPR46437.2021.00236"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Tian, S., Tang, H., Dai, L.: Coupled patch similarity network for one-shot fine-grained image recognition. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 2478\u20132482. IEEE (2021)","DOI":"10.1109\/ICIP42928.2021.9506685"},{"key":"2_CR23","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":"2_CR24","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-UCSD birds-200-2011 dataset (2011)"},{"issue":"12","key":"2_CR25","doi-asserted-by":"publisher","first-page":"6116","DOI":"10.1109\/TIP.2019.2924811","volume":"28","author":"XS Wei","year":"2019","unstructured":"Wei, X.S., Wang, P., Liu, L., Shen, C., Wu, J.: Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples. IEEE Trans. Image Process. 28(12), 6116\u20136125 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Object-aware long-short-range spatial alignment for few-shot fine-grained image classification. arXiv preprint arXiv:2108.13098 (2021)","DOI":"10.1145\/3474085.3475532"},{"key":"2_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/978-3-030-67832-6_12","volume-title":"MultiMedia Modeling","author":"F Zhang","year":"2021","unstructured":"Zhang, F., Li, M., Zhai, G., Liu, Y.: Multi-branch and multi-scale attention learning for fine-grained visual categorization. In: Loko\u010d, J., Skopal, T., Schoeffmann, K., Mezaris, V., Li, X., Vrochidis, S., Patras, I. (eds.) MMM 2021, Part I. LNCS, vol. 12572, pp. 136\u2013147. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67832-6_12"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Zhu, H., Gao, Z., Wang, J., Zhou, Y., Li, C.: Few-shot fine-grained image classification via multi-frequency neighborhood and double-cross modulation. arXiv preprint arXiv:2207.08547 (2022)","DOI":"10.3390\/s22197640"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, C., Jiang, S.: Multi-attention meta learning for few-shot fine-grained image recognition. In: IJCAI, pp. 1090\u20131096 (2020)","DOI":"10.24963\/ijcai.2020\/152"},{"key":"2_CR30","unstructured":"Ziko, I., Dolz, J., Granger, E., Ayed, I.B.: Laplacian regularized few-shot learning. In: International Conference on Machine Learning, pp. 11660\u201311670. PMLR (2020)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44201-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T08:03:38Z","timestamp":1695369818000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44201-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442001","9783031442018"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44201-8_2","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":"23 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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)"}},{"value":"type of other papers accepted  : 9 Abstract","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)"}}]}}