{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:22:25Z","timestamp":1763018545560,"version":"3.40.3"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200526"},{"type":"electronic","value":"9783031200533"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20053-3_11","type":"book-chapter","created":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T16:21:52Z","timestamp":1667665312000},"page":"179-196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Towards Calibrated Hyper-Sphere Representation via\u00a0Distribution Overlap Coefficient for\u00a0Long-Tailed Learning"],"prefix":"10.1007","author":[{"given":"Hualiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siming","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoxuan","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hangxiang","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuozhu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoji","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,6]]},"reference":[{"issue":"5","key":"11_CR1","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/TPAMI.2019.2956516","volume":"43","author":"Z Cai","year":"2019","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1483\u20131498 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR2","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"11_CR3","unstructured":"Chen, K., et al.: Mmdetection: open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155 (2019)"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Cui, J., Zhong, Z., Liu, S., Yu, B., Jia, J.: Parametric contrastive learning (2021)","DOI":"10.1109\/ICCV48922.2021.00075"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Dhaker, H., Ngom, P., Mbodj, M.: Overlap coefficients based on kullback-leibler divergence: exponential populations case. Int. J. Appl. Math. Res. 6(4) (2017)","DOI":"10.14419\/ijamr.v6i4.8493"},{"key":"11_CR8","unstructured":"Diethe, T.: A note on the kullback-leibler divergence for the von mises-fisher distribution. arXiv preprint arXiv:1502.07104 (2015)"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Gupta, A., Dollar, P., Girshick, R.: Lvis: a dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5356\u20135364 (2019)","DOI":"10.1109\/CVPR.2019.00550"},{"key":"11_CR10","unstructured":"Hasnat, M., Bohn\u00e9, J., Milgram, J., Gentric, S., Chen, L., et al.: von mises-fisher mixture model-based deep learning: application to face verification. arXiv preprint arXiv:1706.04264 (2017)"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Hong, Y., Han, S., Choi, K., Seo, S., Kim, B., Chang, B.: Disentangling label distribution for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6626\u20136636 (2021)","DOI":"10.1109\/CVPR46437.2021.00656"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, Y., Loy, C.C., Tang, X.: Learning deep representation for imbalanced classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5375\u20135384 (2016)","DOI":"10.1109\/CVPR.2016.580"},{"issue":"3","key":"11_CR15","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1214\/aos\/1176344681","volume":"7","author":"PE Jupp","year":"1979","unstructured":"Jupp, P.E., Mardia, K.V.: Maximum likelihood estimators for the matrix von mises-fisher and bingham distributions. Ann. Stat. 7(3), 599\u2013606 (1979)","journal-title":"Ann. Stat."},{"key":"11_CR16","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition (2019)"},{"key":"11_CR17","unstructured":"Kang, B., Li, Y., Xie, S., Yuan, Z., Feng, J.: Exploring balanced feature spaces for representation learning. In: International Conference on Learning Representations (2021)"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Kent, J.: Some probabilistic properties of bessel functions. Ann. Probabil., 760\u2013770 (1978)","DOI":"10.1214\/aop\/1176995427"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Kobayashi, T.: t-vmf similarity for regularizing intra-class feature distribution. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6612\u20136621 (2021)","DOI":"10.1109\/CVPR46437.2021.00655"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Li, S., Xu, J., Xu, X., Shen, P., Li, S., Hooi, B.: Spherical confidence learning for face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15629\u201315637 (2021)","DOI":"10.1109\/CVPR46437.2021.01537"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Li, T., et al.: Targeted supervised contrastive learning for long-tailed recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6918\u20136928 (2022)","DOI":"10.1109\/CVPR52688.2022.00679"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Overcoming classifier imbalance for long-tail object detection with balanced group softmax. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10991\u201311000 (2020)","DOI":"10.1109\/CVPR42600.2020.01100"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Liu, B., Li, H., Kang, H., Hua, G., Vasconcelos, N.: Gistnet: a geometric structure transfer network for long-tailed recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8209\u20138218 (2021)","DOI":"10.1109\/ICCV48922.2021.00810"},{"key":"11_CR25","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: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00264"},{"key":"11_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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2537\u20132546 (2019)","DOI":"10.1109\/CVPR.2019.00264"},{"key":"11_CR27","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Mash\u2019al, M., Hosseini, R.: K-means++ for mixtures of von mises-fisher distributions. In: 2015 7th Conference on Information and Knowledge Technology (IKT), pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/IKT.2015.7288786"},{"issue":"26","key":"11_CR29","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.5694\/j.1326-5377.1971.tb92876.x","volume":"2","author":"E Nicholls","year":"1971","unstructured":"Nicholls, E., Stark, A.: Bayes\u2019 theorem. Med. J. Aust. 2(26), 1335\u20131339 (1971)","journal-title":"Med. J. Aust."},{"key":"11_CR30","unstructured":"Papadopoulos, C.I.: On the Kullback-Leibler information measure and statistical inference. Wayne State University (1971)"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Peng, Z., Huang, W., Guo, Z., Zhang, X., Jiao, J., Ye, Q.: Long-tailed distribution adaptation. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3275\u20133282 (2021)","DOI":"10.1145\/3474085.3475479"},{"key":"11_CR32","first-page":"4175","volume":"33","author":"J Ren","year":"2020","unstructured":"Ren, J., Yu, C., Ma, X., Zhao, H., Yi, S., et al.: Balanced meta-softmax for long-tailed visual recognition. Adv. Neural Inf. Process. Syst. 33, 4175\u20134186 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Romanazzi, M.: Discriminant analysis with high dimensional von mises-fisher distributions. In: 8th Annual International Conference on Statistics, pp. 1\u201316. Athens Institute for Education and Research (2014)","DOI":"10.30958\/ajs.1-4-1"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Samuel, D., Chechik, G.: Distributional robustness loss for long-tail learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00936"},{"key":"11_CR35","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1685\u20131694 (2021)","DOI":"10.1109\/CVPR46437.2021.00173"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Tan, J., et al.: Equalization loss for long-tailed object recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11662\u201311671 (2020)","DOI":"10.1109\/CVPR42600.2020.01168"},{"key":"11_CR37","unstructured":"Tang, K., Huang, J., Zhang, H.: Long-tailed classification by keeping the good and removing the bad momentum causal effect. In: NeurIPS (2020)"},{"key":"11_CR38","first-page":"1513","volume":"33","author":"K Tang","year":"2020","unstructured":"Tang, K., Huang, J., Zhang, H.: Long-tailed classification by keeping the good and removing the bad momentum causal effect. Adv. Neural Inf. Process. Syst. 33, 1513\u20131524 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Van Horn, G., et al.: The inaturalist species classification and detection dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8769\u20138778 (2018)","DOI":"10.1109\/CVPR.2018.00914"},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Seesaw loss for long-tailed instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00957"},{"key":"11_CR41","doi-asserted-by":"crossref","unstructured":"Wang, P., Han, K., Wei, X.S., Zhang, L., Wang, L.: Contrastive learning based hybrid networks for long-tailed image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 943\u2013952 (2021)","DOI":"10.1109\/CVPR46437.2021.00100"},{"key":"11_CR42","unstructured":"Wang, X., Lian, L., Miao, Z., Liu, Z., Yu, S.: Long-tailed recognition by routing diverse distribution-aware experts. In: International Conference on Learning Representations (2021)"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Weng, Z., Ogut, M.G., Limonchik, S., Yeung, S.: Unsupervised discovery of the long-tail in instance segmentation using hierarchical self-supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2603\u20132612 (2021)","DOI":"10.1109\/CVPR46437.2021.00263"},{"key":"11_CR44","doi-asserted-by":"crossref","unstructured":"Wu, T., Liu, Z., Huang, Q., Wang, Y., Lin, D.: Adversarial robustness under long-tailed distribution (2021)","DOI":"10.1109\/CVPR46437.2021.00855"},{"key":"11_CR45","unstructured":"Wu, T.Y., Morgado, P., Wang, P., Ho, C.H., Vasconcelos, N.: Solving long-tailed recognition with deep realistic taxonomic classifier"},{"key":"11_CR46","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. arXiv preprint arXiv:1611.05431 (2016)","DOI":"10.1109\/CVPR.2017.634"},{"key":"11_CR47","first-page":"19290","volume":"33","author":"Y Yang","year":"2020","unstructured":"Yang, Y., Xu, Z.: Rethinking the value of labels for improving class-imbalanced learning. Adv. Neural Inf. Process. Syst. 33, 19290\u201319301 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"11_CR48","doi-asserted-by":"crossref","unstructured":"Ye, H.J., Chen, H.Y., Zhan, D.C., Chao, W.L.: Identifying and compensating for feature deviation in imbalanced deep learning (2020)","DOI":"10.1109\/ICCV48922.2021.00016"},{"key":"11_CR49","unstructured":"Yuan, Y., Wang, J.: Ocnet: object context network for scene parsing (2018)"},{"key":"11_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, Z., Yan, S., He, X., Sun, J.: Distribution alignment: a unified framework for long-tail visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2361\u20132370 (2021)","DOI":"10.1109\/CVPR46437.2021.00239"},{"key":"11_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, X., Fang, Z., Wen, Y., Li, Z., Qiao, Y.: Range loss for deep face recognition with long-tailed training data. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5409\u20135418 (2017)","DOI":"10.1109\/ICCV.2017.578"},{"key":"11_CR52","unstructured":"Zhang, Y., Hooi, B., Hong, L., Feng, J.: Test-agnostic long-tailed recognition by test-time aggregating diverse experts with self-supervision. arXiv preprint arXiv:2107.09249 (2021)"},{"key":"11_CR53","unstructured":"Zhang, Y., Kang, B., Hooi, B., Yan, S., Feng, J.: Deep long-tailed learning: a survey. arXiv preprint arXiv:2110.04596 (2021)"},{"key":"11_CR54","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, pp. 16489\u201316498 (2021)","DOI":"10.1109\/CVPR46437.2021.01622"},{"key":"11_CR55","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 633\u2013641 (2017)","DOI":"10.1109\/CVPR.2017.544"},{"key":"11_CR56","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9719\u20139728 (2020)","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"11_CR57","doi-asserted-by":"crossref","unstructured":"Zhu, B., Niu, Y., Hua, X.S., Zhang, H.: Cross-domain empirical risk minimization for unbiased long-tailed classification. In: AAAI Conference on Artificial Intelligence (2022)","DOI":"10.1609\/aaai.v36i3.20271"},{"key":"11_CR58","doi-asserted-by":"crossref","unstructured":"Zhu, L., Yang, Y.: Inflated episodic memory with region self-attention for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4344\u20134353 (2020)","DOI":"10.1109\/CVPR42600.2020.00440"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20053-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T16:24:53Z","timestamp":1667665493000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20053-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200526","9783031200533"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20053-3_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 November 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}