{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T07:48:53Z","timestamp":1781596133889,"version":"3.54.5"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200731","type":"print"},{"value":"9783031200748","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\/978-3-031-20074-8_10","type":"book-chapter","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:23:11Z","timestamp":1668198191000},"page":"163-180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["OOD-CV: A Benchmark for\u00a0Robustness to\u00a0Out-of-Distribution Shifts of\u00a0Individual Nuisances in\u00a0Natural Images"],"prefix":"10.1007","author":[{"given":"Bingchen","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaozuo","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wufei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shenxiao","family":"Mei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angtian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan","family":"Yuille","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Kortylewski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"10_CR1","unstructured":"Robust Vision Challenge 2020. http:\/\/www.robustvision.net\/"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Alcorn, M.A., et al.: Strike (with) a pose: neural networks are easily fooled by strange poses of familiar objects. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4845\u20134854 (2019)","DOI":"10.1109\/CVPR.2019.00498"},{"key":"10_CR3","first-page":"26831","volume":"34","author":"Y Bai","year":"2021","unstructured":"Bai, Y., Mei, J., Yuille, A.L., Xie, C.: Are Transformers more robust than CNNs? Adv. Neural Inf. Process. Syst. 34, 26831\u201326843 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"7","key":"10_CR4","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1145\/3448250","volume":"64","author":"Y Bengio","year":"2021","unstructured":"Bengio, Y., Lecun, Y., Hinton, G.: Deep learning for AI. Commun. ACM 64(7), 58\u201365 (2021)","journal-title":"Commun. ACM"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Bhojanapalli, S., Chakrabarti, A., Glasner, D., Li, D., Unterthiner, T., Veit, A.: Understanding robustness of transformers for image classification. In: International Conference on Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.01007"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Borji, A., Izadi, S., Itti, L.: ilab-20m: a large-scale controlled object dataset to investigate deep learning. In: IEEE Conference on Computer Vision Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.244"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: IEEE Conference Computer Vision Pattern Recognition (2014)","DOI":"10.1109\/CVPR.2014.254"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation policies from data. In: IEEE Conference on Computer Vision Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2019.00020"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Cui, Q., et al. Discriminability-transferability trade-off: an information-theoretic perspective. In: European Conference on Computer Vision (2022)","DOI":"10.1007\/978-3-031-19809-0_2"},{"key":"10_CR10","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: IEEE Conference Computer Vision Pattern Recognition (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10_CR11","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. In: International Conference Learning Representation (2020)"},{"key":"10_CR12","unstructured":"Erichson, N.B., Lim, S.H., Utrera, F., Xu, W., Cao, Z., Mahoney, M.W.: Noisymix: boosting robustness by combining data augmentations, stability training, and noise injections. arXiv preprint arXiv:2202.01263, 2022"},{"issue":"1","key":"10_CR13","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S.M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98\u2013136 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"10_CR14","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: IEEE Conference on Computer Vision Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.265"},{"key":"10_CR16","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: International Conference on Learning Representation (2019)"},{"key":"10_CR17","unstructured":"Gulrajani,I., Lopez-Paz, D.: In search of lost domain generalization. In: International Conference Learning Representation (2021)"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference Computer Vision Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., et al. The many faces of robustness: a critical analysis of out-of-distribution generalization. In: International Conference on Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"10_CR20","unstructured":"Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. In: International Conference on Learning Representation (2019)"},{"key":"10_CR21","unstructured":"Hendrycks, D., Mazeika, M., Kadavath, S., Song, D.: Using self-supervised learning can improve model robustness and uncertainty. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"10_CR22","unstructured":"Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: Augmix: a simple data processing method to improve robustness and uncertainty. In: International Conference on Learning Representation (2020)"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., Song, D.: Natural adversarial examples. In: IEEE Conference on Computer Vision Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01501"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Howard, A., et al. Searching for mobilenetv3. In: International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"10_CR25","unstructured":"Koh, P.W., et al. Wilds: a benchmark of in-the-wild distribution shifts. In: International Conference on Machine Learning (2021)"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Morel-Forster, A., Vetter, T.: Empirically analyzing the effect of dataset biases on deep face recognition systems. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00283"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Morel-Forster, A., Vetter, T.: Analyzing and reducing the damage of dataset bias to face recognition with synthetic data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00279"},{"issue":"3","key":"10_CR28","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1007\/s11263-020-01401-3","volume":"129","author":"A Kortylewski","year":"2021","unstructured":"Kortylewski, A., Liu, Q., Wang, A., Sun, Y., Yuille, A.: Compositional convolutional neural networks: a robust and interpretable model for object recognition under occlusion. Int. J. Comput. Vision 129(3), 736\u2013760 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"10_CR29","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. In: International Conference Learning Representation (2017)"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"10_CR31","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":"10_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al. Swin transformer: hierarchical vision transformer using shifted windows. In: International Conference on Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10_CR33","first-page":"11525","volume":"33","author":"L Francesco","year":"2020","unstructured":"Francesco, L., et al.: Object-centric learning with slot attention. Adv. Neural Inform. Process. Syst. 33, 11525\u201311538 (2020)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Mahmood, K., Mahmood, R., Van Dijk, M.: On the robustness of vision transformers to adversarial examples. In: International Conference on Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.00774"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Michaelis, C., et al. Benchmarking robustness in object detection: autonomous driving when winter is coming. Adv. Neural Inf. Process. Syst. (2019)","DOI":"10.12792\/icisip2019.002"},{"key":"10_CR36","unstructured":"Michaelis, C., et al. Benchmarking robustness in object detection: autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484 (2019)"},{"key":"10_CR37","doi-asserted-by":"crossref","unstructured":"Mohseni, S., Wang, H., Zhiding, Y., Xiao, C., Wang, Z., Yadawa, J.: Practical machine learning safety: a survey and primer. ArXiv (2021)","DOI":"10.1145\/3551385"},{"key":"10_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/978-3-319-49409-8_75","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"W Qiu","year":"2016","unstructured":"Qiu, W., Yuille, A.: UnrealCV: connecting computer vision to unreal engine. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 909\u2013916. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_75"},{"key":"10_CR39","unstructured":"Recht, B., Roelofs, R., Schmidt, L., Shankar, V.: Do imagenet classifiers generalize to imagenet? In: International Conference Machine Learning (2019)"},{"key":"10_CR40","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. Adv. Neural Inf. Processing Syst. 28 (2015)"},{"key":"10_CR41","unstructured":"Rosenfeld, A., Zemel, R., Tsotsos, J.K.: The elephant in the room. arXiv preprint arXiv:1808.03305 (2018)"},{"key":"10_CR42","doi-asserted-by":"crossref","unstructured":"Shao, J., Wen, X., Zhao, B., Xue, X.: Temporal context aggregation for video retrieval with contrastive learning. In: IEEE Winter Conference on Applications of Computer Vision (2021)","DOI":"10.1109\/WACV48630.2021.00331"},{"key":"10_CR43","doi-asserted-by":"crossref","unstructured":"Tang, K., Tao, M., Qi, J., Liu, Z., Zhang, H.: Invariant feature learning for generalized long-tailed classification. In: Europe Confernce on Computer Vision (2022)","DOI":"10.1007\/978-3-031-20053-3_41"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Tremblay, J., To, T., Birchfield, S.: Falling things: a synthetic dataset for 3D object detection and pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00275"},{"key":"10_CR45","unstructured":"Wang, A., Kortylewski, A., Yuille, A.: Nemo: neural mesh models of contrastive features for robust 3D pose estimation. In: International Conference on Learning Representation (2021)"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Wang, A., Sun, Y., Kortylewski, A., Yuille, A.L.: Robust object detection under occlusion with context-aware compositional nets. In: IEEE Conference on Computer Vision Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.01266"},{"key":"10_CR47","unstructured":"Wang, H., Xiao, C., Kossaifi, J., Zhiding, Y., Anandkumar, A., Wang, Z.: Augmax: adversarial composition of random augmentations for robust training. In: NeurIPS (2021)"},{"key":"10_CR48","unstructured":"Wen, X., Zhao, B., Zheng, A., Zhang, X., Qi, X.: Self-supervised visual representation learning with semantic grouping (2022). arxiv: 2205.15288"},{"key":"10_CR49","unstructured":"Wong, E., Rice, L., Kolter, J.Z.: Represent, fast is better than free: revisiting adversarial training. In: International Conference on Learning (2020)"},{"key":"10_CR50","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Mottaghi, R., Savarese, S.: Beyond pascal: a benchmark for 3D object detection in the wild. In: IEEE Winter Conference on Applications of Computer Vision (2014)","DOI":"10.1109\/WACV.2014.6836101"},{"key":"10_CR51","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: Posecnn: a convolutional neural network for 6D object pose estimation in cluttered scenes. In Robotics: Science and Systems (RSS) (2018)","DOI":"10.15607\/RSS.2018.XIV.019"},{"key":"10_CR52","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/978-3-030-66096-3_31","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"M Xiao","year":"2020","unstructured":"Xiao, M., Kortylewski, A., Wu, R., Qiao, S., Shen, W., Yuille, A.: TDMPNet: prototype network with recurrent top-down modulation for robust object classification under partial occlusion. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12536, pp. 447\u2013463. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66096-3_31"},{"key":"10_CR53","doi-asserted-by":"crossref","unstructured":"Xie, C., Wu, Y., van der Maaten, L., Yuille, A.L., He, K.: Feature denoising for improving adversarial robustness. In IEEE Conference on Computer Vision Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00059"},{"key":"10_CR54","doi-asserted-by":"crossref","unstructured":"Ye, N., et al.: Ood-bench: benchmarking and understanding out-of-distribution generalization datasets and algorithms. arXiv preprint arXiv:2106.03721 (2021)","DOI":"10.1109\/CVPR52688.2022.00779"},{"key":"10_CR55","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"10_CR56","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1007\/978-3-030-66096-3_29","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"B Zhao","year":"2020","unstructured":"Zhao, B., Wen, X.: Distilling visual priors from self-supervised learning. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12536, pp. 422\u2013429. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66096-3_29"},{"key":"10_CR57","doi-asserted-by":"crossref","unstructured":"Zhou, X., Karpur, A., Luo, L., Huang, Q.: Starmap for category-agnostic keypoint and viewpoint estimation. In: European Conference on Computer Vision (2018)","DOI":"10.1007\/978-3-030-01246-5_20"},{"key":"10_CR58","doi-asserted-by":"crossref","unstructured":"Zhu, R., Zhao, B., Liu, J., Sun, Z., Chen, C.W.: Improving contrastive learning by visualizing feature transformation. In: International Conference Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.01014"}],"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\/978-3-031-20074-8_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:26:27Z","timestamp":1668198387000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20074-8_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200731","9783031200748"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20074-8_10","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":"12 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)"}}]}}