{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:37:48Z","timestamp":1780501068255,"version":"3.54.1"},"publisher-location":"Cham","reference-count":68,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197772","type":"print"},{"value":"9783031197789","type":"electronic"}],"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\/978-3-031-19778-9_16","type":"book-chapter","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:28:41Z","timestamp":1667420921000},"page":"270-288","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Discover and\u00a0Mitigate Unknown Biases with\u00a0Debiasing Alternate Networks"],"prefix":"10.1007","author":[{"given":"Zhiheng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Hoogs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenliang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"16_CR1","unstructured":"Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: International Conference on Very Large Data Bases (1994)"},{"key":"16_CR2","unstructured":"Ahmed, F., Bengio, Y., van Seijen, H., Courville, A.: Systematic generalisation with group invariant predictions. In: International Conference on Learning Representations (2021)"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Albiero, V., KS, K., Vangara, K., Zhang, K., King, M.C., Bowyer, K.W.: Analysis of gender inequality in face recognition accuracy. In: The IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW) (2020)","DOI":"10.1109\/WACVW50321.2020.9096947"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Alvi, M., Zisserman, A., Nellaaker, C.: Turning a blind eye: explicit removal of biases and variation from deep neural network embeddings. In: The European Conference on Computer Vision Workshop (ECCVW) (2018)","DOI":"10.1007\/978-3-030-11009-3_34"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Antol, S., et al.: VQA: visual question answering. In: The IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.279"},{"key":"16_CR6","unstructured":"Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: invariant risk minimization. arXiv:1907.02893 [cs, stat] (2020)"},{"key":"16_CR7","unstructured":"Bahng, H., Chun, S., Yun, S., Choo, J., Oh, S.J.: Learning de-biased representations with biased representations. In: International Conference on Machine Learning (2020)"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Balakrishnan, G., Xiong, Y., Xia, W., Perona, P.: Towards causal benchmarking of bias in face analysis algorithms. In: The European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58523-5_32"},{"key":"16_CR9","unstructured":"Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: ACM Conference on Fairness, Accountability, and Transparency (2018)"},{"key":"16_CR10","unstructured":"Cadene, R., Dancette, C., Ben younes, H., Cord, M., Parikh, D.: RUBi: reducing unimodal biases for visual question answering. In: Advances in Neural Information Processing Systems (2019)"},{"key":"16_CR11","unstructured":"Choi, J., Gao, C., Messou, J.C.E., Huang, J.B.: Why can\u2019t i dance in the mall? Learning to mitigate scene bias in action recognition. In: Advances in Neural Information Processing Systems (2019)"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Clark, C., Yatskar, M., Zettlemoyer, L.: don\u2019t take the easy way out: ensemble based methods for avoiding known dataset biases. In: Empirical Methods in Natural Language Processing (2019)","DOI":"10.18653\/v1\/D19-1418"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., Huq, A.: Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017)","DOI":"10.1145\/3097983.3098095"},{"key":"16_CR14","unstructured":"Creager, E., Jacobsen, J.H., Zemel, R.: Environment inference for invariant learning. In: International Conference on Machine Learning (2021)"},{"key":"16_CR15","unstructured":"Creager, E., et al.: Flexibly fair representation learning by disentanglement. In: International Conference on Machine Learning (2019)"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Dhar, P., Gleason, J., Roy, A., Castillo, C.D., Chellappa, R.: PASS: protected attribute suppression system for mitigating bias in face recognition. In: The IEEE International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01481"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (2012)","DOI":"10.1145\/2090236.2090255"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Geirhos, R., et al.: Shortcut learning in deep neural networks. Nat. Mach. Intell. 2(11), 665\u2013673 (2020)","DOI":"10.1038\/s42256-020-00257-z"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Gong, S., Liu, X., Jain, A.K.: Jointly de-biasing face recognition and demographic attribute estimation. In: The European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58526-6_20"},{"key":"16_CR20","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)"},{"key":"16_CR21","unstructured":"Grgic-Hlaca, N., Zafar, M.B., Gummadi, K.P., Weller, A.: The case for process fairness in learning: Feature selection for fair decision making. In: NIPS Symposium on Machine Learning and the Law (2016)"},{"key":"16_CR22","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems (2016)"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Hazirbas, C., Bitton, J., Dolhansky, B., Pan, J., Gordo, A., Ferrer, C.C.: Towards measuring fairness in AI: the casual conversations dataset. arXiv:2104.02821 [cs] (2021)","DOI":"10.1109\/TBIOM.2021.3132237"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Hendricks, L.A., Burns, K., Saenko, K., Darrell, T., Rohrbach, A.: Women also snowboard: overcoming bias in captioning models. In: The European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01219-9_47"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Jia, S., Meng, T., Zhao, J., Chang, K.W.: Mitigating gender bias amplification in distribution by posterior regularization. In: Annual Meeting of the Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.264"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Joo, J., K\u00e4rkk\u00e4inen, K.: Gender slopes: counterfactual fairness for computer vision models by attribute manipulation. In: International Workshop on Fairness, Accountability, Transparency and Ethics in Multimedia (2020)","DOI":"10.1145\/3422841.3423533"},{"key":"16_CR28","doi-asserted-by":"publisher","unstructured":"Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33, 1\u201333 (2012). https:\/\/doi.org\/10.1007\/s10115-011-0463-8","DOI":"10.1007\/s10115-011-0463-8"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Kim, B., Kim, H., Kim, K., Kim, S., Kim, J.: Learning not to learn: training deep neural networks with biased data. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00922"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Kim, E., Lee, J., Choo, J.: BiaSwap: removing dataset bias with bias-tailored swapping augmentation. In: The IEEE International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01472"},{"key":"16_CR33","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"16_CR34","unstructured":"Krishnakumar, A., Prabhu, V., Sudhakar, S., Hoffman, J.: UDIS: unsupervised discovery of bias in deep visual recognition models. In: British Machine Vision Conference, BMVC (2021)"},{"key":"16_CR35","unstructured":"Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. In: Advances in Neural Information Processing Systems (2017)"},{"key":"16_CR36","unstructured":"Lahoti, P., et al.: Fairness without demographics through adversarially reweighted learning. In: Advances in Neural Information Processing Systems (2020)"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Lang, O., et al.: Explaining in style: training a GAN to explain a classifier in StyleSpace. In: The IEEE International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00073"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE (1998)","DOI":"10.1109\/5.726791"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Li, W., et al.: Object-driven text-to-image synthesis via adversarial training. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.01245"},{"key":"16_CR40","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Y., Vasconcelos, N.: RESOUND: towards action recognition without Representation Bias. In: The European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01231-1_32"},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Li, Y., Vasconcelos, N.: REPAIR: removing representation bias by dataset resampling. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00980"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Li, Z., Xu, C.: Discover the unknown biased attribute of an image classifier. In: The IEEE International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01470"},{"key":"16_CR43","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: The IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"16_CR44","doi-asserted-by":"crossref","unstructured":"Manjunatha, V., Saini, N., Davis, L.S.: Explicit bias discovery in visual question answering models. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00979"},{"key":"16_CR45","unstructured":"Nam, J., Cha, H., Ahn, S., Lee, J., Shin, J.: Learning from failure: training debiased classifier from biased classifier. In: Advances in Neural Information Processing Systems (2020)"},{"key":"16_CR46","unstructured":"Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., Weinberger, K.Q.: On fairness and calibration. In: Advances in Neural Information Processing Systems (2017)"},{"key":"16_CR47","unstructured":"Sagawa*, S., Koh*, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks for group shifts: on the importance of regularization for worst-case generalization. In: International Conference on Learning Representations (2020)"},{"key":"16_CR48","doi-asserted-by":"crossref","unstructured":"Sarhan, M.H., Navab, N., Albarqouni, S.: Fairness by learning orthogonal disentangled representations. In: The European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58526-6_44"},{"key":"16_CR49","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: The IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.74"},{"issue":"2","key":"16_CR50","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128(2), 336\u2013359 (2020). https:\/\/doi.org\/10.1007\/s11263-019-01228-7","journal-title":"Int. J. Comput. Vis."},{"key":"16_CR51","doi-asserted-by":"crossref","unstructured":"Singh, K.K., Mahajan, D., Grauman, K., Lee, Y.J., Feiszli, M., Ghadiyaram, D.: Don\u2019t judge an object by its context: learning to overcome contextual bias. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01108"},{"key":"16_CR52","unstructured":"Sohoni, N.S., Dunnmon, J.A., Angus, G., Gu, A., R\u00e9, C.: No subclass left behind: fine-grained robustness in coarse-grained classification problems. In: Advances in Neural Information Processing Systems (2020)"},{"key":"16_CR53","doi-asserted-by":"crossref","unstructured":"Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"16_CR54","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: The IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.463"},{"key":"16_CR55","doi-asserted-by":"crossref","unstructured":"Verma, S., Rubin, J.: Fairness definitions explained. In: 2018 IEEE\/ACM International Workshop on Software Fairness (FairWare) (2018)","DOI":"10.1145\/3194770.3194776"},{"key":"16_CR56","doi-asserted-by":"crossref","unstructured":"Wang, A., Narayanan, A., Russakovsky, O.: REVISE: a tool for measuring and mitigating bias in image datasets. In: The European Conference on Computer Vision (ECCV) (2020a)","DOI":"10.1007\/978-3-030-58580-8_43"},{"key":"16_CR57","unstructured":"Wang, H., He, Z., Lipton, Z.C., Xing, E.P.: Learning robust representations by projecting superficial statistics out. In: International Conference on Learning Representations (2019a)"},{"key":"16_CR58","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, Y., Wang, X.E.: Are gender-neutral queries really gender-neutral? mitigating gender bias in image search. In: Empirical Methods in Natural Language Processing (2021a)","DOI":"10.18653\/v1\/2021.emnlp-main.151"},{"key":"16_CR59","unstructured":"Wang, T., Yue, Z., Huang, J., Sun, Q., Zhang, H.: Self-supervised learning disentangled group representation as feature. In: Advances in Neural Information Processing Systems (2021b)"},{"key":"16_CR60","doi-asserted-by":"crossref","unstructured":"Wang, X., Ang, M.H., Lee, G.H.: Cascaded refinement network for point cloud completion. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020b)","DOI":"10.1109\/CVPR42600.2020.00087"},{"key":"16_CR61","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: CAMP: cross-modal adaptive message passing for text-image retrieval. In: The IEEE International Conference on Computer Vision (ICCV) (2019b)","DOI":"10.1109\/ICCV.2019.00586"},{"key":"16_CR62","doi-asserted-by":"crossref","unstructured":"Wang, Z., Qinami, K., Karakozis, I.C., Genova, K., Nair, P., Hata, K., Russakovsky, O.: Towards fairness in visual recognition: effective strategies for bias mitigation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020c)","DOI":"10.1109\/CVPR42600.2020.00894"},{"key":"16_CR63","unstructured":"Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., Xiao, J.: LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv:1506.03365 [cs] (2016)"},{"key":"16_CR64","doi-asserted-by":"crossref","unstructured":"Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: AAAI\/ACM Conference on AI, Ethics, and Society (2018)","DOI":"10.1145\/3278721.3278779"},{"key":"16_CR65","unstructured":"Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: Advances in Neural Information Processing Systems (2018)"},{"key":"16_CR66","doi-asserted-by":"crossref","unstructured":"Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Men also like shopping: reducing gender bias amplification using corpus-level constraints. In: Empirical Methods in Natural Language Processing (2017)","DOI":"10.18653\/v1\/D17-1323"},{"key":"16_CR67","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.319"},{"issue":"6","key":"16_CR68","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2018","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452\u20131464 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"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\/978-3-031-19778-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:54:51Z","timestamp":1667422491000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19778-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197772","9783031197789"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19778-9_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"3 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)"}}]}}