{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T15:37:34Z","timestamp":1776872254079,"version":"3.51.2"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031851803","type":"print"},{"value":"9783031851810","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-85181-0_21","type":"book-chapter","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T06:58:59Z","timestamp":1745305139000},"page":"329-343","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Uncertainty Voting Ensemble for\u00a0Imbalanced Deep Regression"],"prefix":"10.1007","author":[{"given":"Yuchang","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivien Sainte Fare","family":"Garnot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konrad","family":"Schindler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan Dirk","family":"Wegner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,23]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.isprsjprs.2022.11.011","volume":"195","author":"A Becker","year":"2023","unstructured":"Becker, A., Russo, S., Puliti, S., Lang, N., Schindler, K., Wegner, J.D.: Country-wide retrieval of forest structure from optical and SAR satellite imagery with deep ensembles. ISPRS J. Photogramm. Remote. Sens. 195, 269\u2013286 (2023)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"21_CR2","unstructured":"Branco, P., Torgo, L., Ribeiro, R.P.: SMOGN: a pre-processing approach for imbalanced regression. In: International Workshop on Learning with Imbalanced Domains: Theory and Applications, pp. 36\u201350 (2017)"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., Specia, L.: Semantic textual similarity-multilingual and cross-lingual focused evaluation. In: International Workshop on Semantic Evaluation (SemEval) (2017)","DOI":"10.18653\/v1\/S17-2001"},{"key":"21_CR4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"21_CR6","unstructured":"Gong, Y., Mori, G., Tung, F.: RankSim: ranking similarity regularization for deep imbalanced regression. arXiv preprint arXiv:2205.15236 (2022)"},{"key":"21_CR7","unstructured":"He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. (2009)"},{"key":"21_CR8","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems (NeurIPS), vol. 30 (2017)"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Lang, N., Jetz, W., Schindler, K., Wegner, J.D.: A high-resolution canopy height model of the earth. Nat. Ecol. Evol. 1\u201312 (2023)","DOI":"10.1038\/s41559-023-02206-6"},{"key":"21_CR10","doi-asserted-by":"publisher","first-page":"4271","DOI":"10.1109\/JSTARS.2020.3011907","volume":"13","author":"M Maskey","year":"2020","unstructured":"Maskey, M., et al.: Deepti: deep-learning-based tropical cyclone intensity estimation system. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 13, 4271\u20134281 (2020)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: AgeDB: the first manually collected, in-the-wild age database. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 51\u201359 (2017)","DOI":"10.1109\/CVPRW.2017.250"},{"key":"21_CR12","unstructured":"Paszke, A., et\u00a0al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 32 (2019)"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Ren, J., Zhang, M., Yu, C., Liu, Z.: Balanced MSE for imbalanced visual regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7926\u20137935 (2022)","DOI":"10.1109\/CVPR52688.2022.00777"},{"issue":"2\u20134","key":"21_CR14","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/s11263-016-0940-3","volume":"126","author":"R Rothe","year":"2018","unstructured":"Rothe, R., Timofte, R., Van Gool, L.: Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Comput. Vision 126(2\u20134), 144\u2013157 (2018)","journal-title":"Int. J. Comput. Vision"},{"key":"21_CR15","doi-asserted-by":"publisher","first-page":"2187","DOI":"10.1007\/s10994-021-06023-5","volume":"110","author":"M Steininger","year":"2021","unstructured":"Steininger, M., Kobs, K., Davidson, P., Krause, A., Hotho, A.: Density-based weighting for imbalanced regression. Mach. Learn. 110, 2187\u20132211 (2021)","journal-title":"Mach. Learn."},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: GLUE: a multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461 (2018)","DOI":"10.18653\/v1\/W18-5446"},{"key":"21_CR17","unstructured":"Wang, H., Ji, Q.: Diversity-enhanced probabilistic ensemble for uncertainty estimation. In: Uncertainty in Artificial Intelligence, pp. 2214\u20132225. PMLR (2023)"},{"key":"21_CR18","unstructured":"Wang, X., Lian, L., Miao, Z., Liu, Z., Yu, S.X.: Long-tailed recognition by routing diverse distribution-aware experts. arXiv preprint arXiv:2010.01809 (2020)"},{"key":"21_CR19","unstructured":"Yang, Y., Zha, K., Chen, Y., Wang, H., Katabi, D.: Delving into deep imbalanced regression. In: International Conference on Machine Learning (ICML), pp. 11842\u201311851 (2021)"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Yeo, T., Kar, O.F., Zamir, A.: Robustness via cross-domain ensembles. In: IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 12189\u201312199 (2021)","DOI":"10.1109\/ICCV48922.2021.01197"},{"key":"21_CR21","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"21_CR22","unstructured":"Zhang, Y., Hooi, B., Hong, L., Feng, J.: Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. arXiv preprint arXiv:2107.09249 (2021)"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, B., Cui, Q., Wei, X.S., Chen, Z.M.: BBN: bilateral-branch network with cumulative learning for long-tailed visual recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9719\u20139728 (2020)","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"21_CR24","unstructured":"Zhou, T., Wang, S., Bilmes, J.A.: Diverse ensemble evolution: curriculum data-model marriage. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-85181-0_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T13:15:40Z","timestamp":1758633340000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-85181-0_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031851803","9783031851810"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-85181-0_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"23 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}