{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:15:47Z","timestamp":1743088547046,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434143"},{"type":"electronic","value":"9783031434150"}],"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\/978-3-031-43415-0_28","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T08:01:51Z","timestamp":1694851311000},"page":"471-487","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Meta-HRNet: A High Resolution Network for\u00a0Coarse-to-Fine Few-Shot Classification"],"prefix":"10.1007","author":[{"given":"Zhaochen","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kedian","family":"Mu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"issue":"2","key":"28_CR1","first-page":"929","volume":"35","author":"A Behera","year":"2021","unstructured":"Behera, A., Wharton, Z., Hewage, P.R.P.G., Bera, A.: Context-aware attentional pooling (cap) for fine-grained visual classification. Proc. AAAI Conf. Artif. Intell. 35(2), 929\u2013937 (2021)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Bukchin, G., et al.: Fine-grained angular contrastive learning with coarse labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8730\u20138740 (2021)","DOI":"10.1109\/CVPR46437.2021.00862"},{"key":"28_CR3","unstructured":"Cantelli, F.P.: Sui confini della probabilit\u00e1. In: Atti del Congresso Internazionale dei Matematici: Bologna del 3 al 10 de settembre di 1928, Vol. 6, 1929 (Comunicazioni, sezione IV (A)-V-VII), pp. 47\u201360 (1929)"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Chen, W., Si, C., Wang, W., Wang, L., Wang, Z., Tan, T.: Few-shot learning with part discovery and augmentation from unlabeled images. In: Zhou, Z.H. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pp. 2271\u20132277. International Joint Conferences on Artificial Intelligence Organization (2021), main Track","DOI":"10.24963\/ijcai.2021\/313"},{"key":"28_CR5","unstructured":"Chen, X., Fan, H., Girshick, R.B., He, K.: Improved baselines with momentum contrastive learning. CoRR abs\/2003.04297 (2020)"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, Z., Xu, H., Darrell, T., Wang, X.: Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9042\u20139051. IEEE, Montreal, QC, Canada (Oct 2021)","DOI":"10.1109\/ICCV48922.2021.00893"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S.: Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4109\u20134118. IEEE, Salt Lake City, UT, USA (Jun 2018)","DOI":"10.1109\/CVPR.2018.00432"},{"key":"28_CR8","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/978-3-031-20044-1_19","volume-title":"European Conference on Computer Vision (ECCV 2022)","author":"B Dong","year":"2022","unstructured":"Dong, B., Zhou, P., Yan, S., Zuo, W.: Self-promoted supervision for few-shot transformer. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) European Conference on Computer Vision (ECCV 2022), pp. 329\u2013347. Springer Nature Switzerland, Cham (2022)"},{"key":"28_CR9","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 1126\u20131135. PMLR (06\u201311 Aug 2017)"},{"key":"28_CR10","unstructured":"dan Guo, D., Tian, L., Zhao, H., Zhou, M., Zha, H.: Adaptive distribution calibration for few-shot learning with hierarchical optimal transport. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022)"},{"key":"28_CR11","unstructured":"Kao, C.H., Chiu, W.C., Chen, P.Y.: MAML is a noisy contrastive learner in classification. In: International Conference on Learning Representations (2022)"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Kim, Y., Ha, J.W.: Contrastive fine-grained class clustering via generative adversarial networks (2022)","DOI":"10.1109\/ICEIC57457.2023.10049982"},{"key":"28_CR13","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 (CVPR), pp. 5331\u20135340 (June 2022)","DOI":"10.1109\/CVPR52688.2022.00526"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Li, S., et al.: Improve unsupervised pretraining for few-label transfer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10201\u201310210 (October 2021)","DOI":"10.1109\/ICCV48922.2021.01004"},{"key":"28_CR15","unstructured":"Luo, X., Xu, J., Xu, Z.: Channel importance matters in few-shot image classification. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. (eds.) Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 162, pp. 14542\u201314559. PMLR (2022)"},{"key":"28_CR16","unstructured":"Ni, R., Shu, M., Souri, H., Goldblum, M., Goldstein, T.: The close relationship between contrastive learning and meta-learning. In: International Conference on Learning Representations (2022)"},{"key":"28_CR17","unstructured":"Oh, J., Kim, S., Ho, N., Kim, J.H., Song, H., Yun, S.Y.: Understanding cross-domain few-shot learning based on domain similarity and few-shot difficulty. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022)"},{"key":"28_CR18","unstructured":"Phoo, C.P., Hariharan, B.: Self-training for few-shot transfer across extreme task differences. In: International Conference on Learning Representations (2021)"},{"key":"28_CR19","unstructured":"Requeima, J., Gordon, J., Bronskill, J., Nowozin, S., Turner, R.E.: Fast and flexible multi-task classification using conditional neural adaptive processes. In: Advances in Neural Information Processing Systems. vol. 32. Curran Associates, Inc. (2019)"},{"key":"28_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"28_CR21","unstructured":"Santurkar, S., Tsipras, D., Madry, A.: BREEDS: Benchmarks for subpopulation shift. In: International Conference on Learning Representations (2021)"},{"issue":"11","key":"28_CR22","first-page":"9594","volume":"35","author":"Z Shen","year":"2021","unstructured":"Shen, Z., Liu, Z., Qin, J., Savvides, M., Cheng, K.T.: Partial is better than all: revisiting fine-tuning strategy for few-shot learning. Proc. AAAI Conf. Artif. Intell. 35(11), 9594\u20139602 (2021)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"28_CR23","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Adv. Neural Inform. Process. Syst. 30 (2017)"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Analysis Mach. Intell. 43(10), 3349\u20133364 (2021)","DOI":"10.1109\/TPAMI.2020.2983686"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Yang, J., Yang, H., Chen, L.: Towards cross-granularity few-shot learning: coarse-to-fine pseudo-labeling with visual-semantic meta-embedding. In: Proceedings of the 29th ACM International Conference on Multimedia,pp. 3005\u20133014. ACM, Virtual Event China (2021)","DOI":"10.1145\/3474085.3475200"},{"key":"28_CR26","unstructured":"Yang, S., Liu, L., Xu, M.: Free lunch for few-shot learning: Distribution calibration. In: International Conference on Learning Representations (2021)"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Ye, H.J., Hu, H., Zhan, D.C., Sha, F.: Few-shot learning via embedding adaptation with set-to-set functions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8808\u20138817 (2020)","DOI":"10.1109\/CVPR42600.2020.00883"},{"issue":"2","key":"28_CR28","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s11633-017-1053-3","volume":"14","author":"B Zhao","year":"2017","unstructured":"Zhao, B., Feng, J., Wu, X., Yan, S.: A survey on deep learning-based fine-grained object classification and semantic segmentation. Int. J. Autom. Comput. 14(2), 119\u2013135 (2017)","journal-title":"Int. J. Autom. Comput."},{"key":"28_CR29","unstructured":"Zhu, M., et al.: Dynamic resolution network. In: Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Adv. Neural Inform. Process. Syst. 34, 27319\u201321330 (2021)"},{"key":"28_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, C., Jiang, S.: Multi-attention Meta Learning for Few-shot Fine-grained Image Recognition. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 1090\u20131096. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (Jul 2020)","DOI":"10.24963\/ijcai.2020\/152"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43415-0_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T08:08:39Z","timestamp":1694851719000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43415-0_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434143","9783031434150"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43415-0_28","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":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This research does not contain any personally identifiable information. All datasets were obtained from public resources. The methods proposed in our paper do not have any potential negative societal impacts. Our methods are safe and cannot be integrated into weapons systems. Our research does not have the potential to damage human rights, economic security, people\u2019s livelihoods, or the environment. This is a basic study and even if the methods are misused, they will not cause social harm.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","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":"829","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":"196","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":"24% - 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":"3.63","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":"4.5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","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)"}}]}}