{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:09:23Z","timestamp":1773414563405,"version":"3.50.1"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031733895","type":"print"},{"value":"9783031733901","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"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\/978-3-031-73390-1_12","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T16:24:01Z","timestamp":1730305441000},"page":"198-214","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Rectify the\u00a0Regression Bias in\u00a0Long-Tailed Object Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6338-4151","authenticated-orcid":false,"given":"Ke","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4685-6600","authenticated-orcid":false,"given":"Minghao","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7648-1263","authenticated-orcid":false,"given":"Jie","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0318-8693","authenticated-orcid":false,"given":"Tianyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2085-7568","authenticated-orcid":false,"given":"Jianxin","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","unstructured":"Alexandridis, K.P., Deng, J., Nguyen, A., Luo, S.: Long-tailed instance segmentation using gumbel optimized loss. In: ECCV. LNCS, vol. 13670, pp. 353\u2013369. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20080-9_21","DOI":"10.1007\/978-3-031-20080-9_21"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: CVPR, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"12_CR3","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: NeurIPS, pp. 1565\u20131576 (2019)"},{"key":"12_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-3-030-01264-9_9","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Caron","year":"2018","unstructured":"Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 139\u2013156. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_9"},{"key":"12_CR5","unstructured":"Chen, K., et al.: MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155 (2019)"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, B., Girshick, R., Dollar, P., Berg, A.C., Kirillov, A.: Boundary iou: improving object-centric image segmentation evaluation. In: CVPR, pp. 15334\u201315342 (2021)","DOI":"10.1109\/CVPR46437.2021.01508"},{"key":"12_CR7","doi-asserted-by":"publisher","unstructured":"Cho, J.H., Kr\u00e4henb\u00fchl, P.: Long-tail detection with effective class-margins. In: ECCV. LNCS, vol. 13668, pp. 698\u2013714. Springer (2022) . https:\/\/doi.org\/10.1007\/978-3-031-20074-8_40","DOI":"10.1007\/978-3-031-20074-8_40"},{"key":"12_CR8","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: CVPR, pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"12_CR9","unstructured":"Dave, A., Doll\u00e1r, P., Ramanan, D., Kirillov, A., Girshick, R.: Evaluating large-vocabulary object detectors: The devil is in the details. arXiv preprint arXiv:2102.01066 (2021)"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Du, Y., Wu, J.: No one left behind: Improving the worst categories in long-tailed learning. In: CVPR, pp. 15804\u201315813 (2023)","DOI":"10.1109\/CVPR52729.2023.01517"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Huang, W.: Exploring classification equilibrium in long-tailed object detection. In: ICCV, pp. 3417\u20133426 (2021)","DOI":"10.1109\/ICCV48922.2021.00340"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Gupta, A., Dollar, P., Girshick, R.: Lvis: a dataset for large vocabulary instance segmentation. In: CVPR, pp. 5356\u20135364 (2019)","DOI":"10.1109\/CVPR.2019.00550"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Gupta, A., Dollar, P., Girshick, R.: Lvis: dataset for large vocabulary instance segmentation. In: CVPR, pp. 5356\u20135364 (2019)","DOI":"10.1109\/CVPR.2019.00550"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: ICCV, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"He, Y.Y., Wu, J., Wei, X.S.: Distilling virtual examples for long-tailed recognition. In: ICCV, pp. 235\u2013244 (2021)","DOI":"10.1109\/ICCV48922.2021.00030"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"He, Y.Y., Zhang, P., Wei, X.S., Zhang, X., Sun, J.: Relieving long-tailed instance segmentation via pairwise class balance. In: CVPR, pp. 7000\u20137009 (2022)","DOI":"10.1109\/CVPR52688.2022.00687"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Hsieh, T., Robb, E., Chen, H., Huang, J.: Droploss for long-tail instance segmentation. In: AAAI, pp. 1549\u20131557 (2021)","DOI":"10.1609\/aaai.v35i2.16246"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Hu, X., Jiang, Y., Tang, K., Chen, J., Miao, C., Zhang, H.: Learning to segment the tail. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01406"},{"key":"12_CR20","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. In: ICLR (2020)"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Li, B., et al.: Equalized focal loss for dense long-tailed object detection. In: CVPR, pp. 6990\u20136999 (2022)","DOI":"10.1109\/CVPR52688.2022.00686"},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Li, M., Cheung, Y.m., Lu, Y.: Long-tailed visual recognition via gaussian clouded logit adjustment. In: CVPR, pp. 6929\u20136938 (2022)","DOI":"10.36227\/techrxiv.17031920.v1"},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Li, T., Wang, L., Wu, G.: Self supervision to distillation for long-tailed visual recognition. In: ICCV, pp. 630\u2013639 (2021)","DOI":"10.1109\/ICCV48922.2021.00067"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Overcoming classifier imbalance for long-tail object detection with balanced group softmax. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01100"},{"key":"12_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: CVPR, pp. 2537\u20132546 (2019)","DOI":"10.1109\/CVPR.2019.00264"},{"key":"12_CR27","unstructured":"Menon, A.K., Jayasumana, S., Rawat, A.S., Jain, H., Veit, A., Kumar, S.: Long-tail learning via logit adjustment. In: ICLR (2021)"},{"key":"12_CR28","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., Lin, D.: Libra r-cnn: towards balanced learning for object detection. In: CVPR, pp. 821\u2013830 (2019)","DOI":"10.1109\/CVPR.2019.00091"},{"key":"12_CR29","unstructured":"Ren, J., et al.: Balanced meta-softmax for long-tailed visual recognition. In: NeurIPS, pp. 4175\u20134186 (2020)"},{"key":"12_CR30","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: NeurIPS, pp. 91\u201399 (2015)"},{"issue":"3","key":"12_CR31","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"12_CR32","doi-asserted-by":"crossref","unstructured":"Singh, B., Davis, L.S.: An analysis of scale invariance in object detection SNIP. In: CVPR, pp. 3578\u20133587 (2018)","DOI":"10.1109\/CVPR.2018.00377"},{"key":"12_CR33","doi-asserted-by":"crossref","unstructured":"Tan, J., Lu, X., Zhang, G., Yin, C., Li, Q.: Equalization loss v2: A new gradient balance approach for long-tailed object detection. In: CVPR, pp. 1685\u20131694 (2021)","DOI":"10.1109\/CVPR46437.2021.00173"},{"key":"12_CR34","doi-asserted-by":"crossref","unstructured":"Tan, J., Wang, C., Li, B., Li, Q., Ouyang, W., Yin, C., Yan, J.: Equalization loss for long-tailed object recognition. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01168"},{"key":"12_CR35","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Seesaw loss for long-tailed instance segmentation. In: CVPR, pp. 9695\u20139704 (2021)","DOI":"10.1109\/CVPR46437.2021.00957"},{"key":"12_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/978-3-030-58568-6_4","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Zhao","year":"2020","unstructured":"Zhao, J., Lu, D., Ma, K., Zhang, Yu., Zheng, Y.: Deep Image Clustering with Category-Style Representation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 54\u201370. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_4"},{"key":"12_CR37","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhu, Y., Zhao, C., Zeng, W., Wang, J., Tang, M.: Adaptive class suppression loss for long-tail object detection. In: CVPR, pp. 3103\u20133112 (2021)","DOI":"10.1109\/CVPR46437.2021.00312"},{"key":"12_CR38","unstructured":"Wang, X., Lian, L., Miao, Z., Liu, Z., Yu, S.: Long-tailed recognition by routing diverse distribution-aware experts. In: ICLR (2021)"},{"key":"12_CR39","doi-asserted-by":"crossref","unstructured":"Wu, J., Song, L., Wang, T., Zhang, Q., Yuan, J.: Forest R-CNN: large-vocabulary long-tailed object detection and instance segmentation. In: ACM MM, pp. 1570\u20131578 (2020)","DOI":"10.1145\/3394171.3413970"},{"key":"12_CR40","unstructured":"Xuan, G., Zhang, W., Chai, P.: EM algorithms of gaussian mixture model and hidden markov model. In: ICIP, pp. 145\u2013148 (2001)"},{"key":"12_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, C., et al.: Mosaicos: A simple and effective use of object-centric images for long-tailed object detection. In: ICCV, pp. 417\u2013427 (2021)","DOI":"10.1109\/ICCV48922.2021.00047"},{"key":"12_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chen, C., Peng, S.: Reconciling object-level and global-level objectives for long-tail detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV, pp. 18982\u201318992 (2023)","DOI":"10.1109\/ICCV51070.2023.01740"},{"key":"12_CR43","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Cui, J., Liu, S., Jia, J.: Improving calibration for long-tailed recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16489\u201316498 (2021)","DOI":"10.1109\/CVPR46437.2021.01622"},{"key":"12_CR44","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: CVPR, pp. 9716\u20139725 (2020)","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"12_CR45","doi-asserted-by":"crossref","unstructured":"Zhu, K., Fu, M., Wu, J.: Multi-label self-supervised learning with scene images. In: ICCV. pp. 6694\u20136703 (2023)","DOI":"10.1109\/ICCV51070.2023.00616"},{"key":"12_CR46","doi-asserted-by":"crossref","unstructured":"Zhu, K., He, Y., Wu, J.: Quantized feature distillation for network quantization. In: AAAI, pp. 11452\u201311460 (2023)","DOI":"10.1609\/aaai.v37i9.26354"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73390-1_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T16:32:52Z","timestamp":1730305972000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73390-1_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"ISBN":["9783031733895","9783031733901"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73390-1_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"31 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}