{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T19:52:49Z","timestamp":1774727569545,"version":"3.50.1"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200762","type":"print"},{"value":"9783031200779","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\/978-3-031-20077-9_13","type":"book-chapter","created":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T16:21:52Z","timestamp":1667665312000},"page":"210-226","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["End-to-End Weakly Supervised Object Detection with\u00a0Sparse Proposal Evolution"],"prefix":"10.1007","author":[{"given":"Mingxiang","family":"Liao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenjun","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialing","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuze","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bailan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qixiang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,6]]},"reference":[{"key":"13_CR1","unstructured":"Alex, K., Ilya, S., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NeurIPS, pp. 1097\u20131115 (2012)"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Arbel\u00e1ez, P.A., Pont-Tuset, J., Barron, J.T., Marqu\u00e9s, F., Malik, J.: Multiscale combinatorial grouping. In: IEEE CVPR, pp. 328\u2013335 (2014)","DOI":"10.1109\/CVPR.2014.49"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Arun, A., Jawahar, C.V., Kumar, M.P.: Dissimilarity coefficient based weakly supervised object detection. In: IEEE CVPR, pp. 9432\u20139441 (2019)","DOI":"10.1109\/CVPR.2019.00966"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Bilen, H., Pedersoli, M., Tuytelaars, T.: Weakly supervised object detection with posterior regularization. In: BMVC, pp. 1997\u20132005 (2014)","DOI":"10.5244\/C.28.52"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Bilen, H., Pedersoli, M., Tuytelaars, T.: Weakly supervised object detection with convex clustering. In: IEEE CVPR, pp. 1081\u20131089 (2015)","DOI":"10.1109\/CVPR.2015.7298711"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Bilen, H., Vedaldi, A.: Weakly supervised deep detection networks. In: IEEE CVPR. pp. 2846\u20132854 (2016)","DOI":"10.1109\/CVPR.2016.311"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Cao, T., Du, L., Zhang, X., Chen, S., Zhang, Y., Wang, Y.: Cat: Weakly supervised object detection with category transfer (2021)","DOI":"10.1109\/ICCV48922.2021.00306"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Carreira, J., Sminchisescu, C.: CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE TPAMI 34(7), 1312\u20131328 (2012)","DOI":"10.1109\/TPAMI.2011.231"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Carreira, J., Sminchisescu, C.: CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE TPAMI 34(7), 1312\u20131328 (2012)","DOI":"10.1109\/TPAMI.2011.231"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Cheng, G., Yang, J., Gao, D., Guo, L., Han, J.: High-quality proposals for weakly supervised object detection. IEEE TIP 29, 5794\u20135804 (2020)","DOI":"10.1109\/TIP.2020.2987161"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Cheng, M., Zhang, Z., Lin, W., Torr, P.H.S.: BING: binarized normed gradients for objectness estimation at 300fps. In: IEEE CVPR, pp. 3286\u20133293 (2014)","DOI":"10.1109\/CVPR.2014.414"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Chong, W., Kaiqi, H., Weiqiang, R., Junge, Z., Steve, M.: Large-scale weakly supervised object localization via latent category learning. IEEE TIP 24(4), 1371\u20131385 (2015)","DOI":"10.1109\/TIP.2015.2396361"},{"key":"13_CR14","doi-asserted-by":"publisher","unstructured":"Wang, C., Ren, W., Huang, K., Tan, T.: Weakly supervised object localization with latent category learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 431\u2013445. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_28","DOI":"10.1007\/978-3-319-10599-4_28"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Diba, A., Sharma, V., Pazandeh, A., Pirsiavash, H., Van Gool, L.: Weakly supervised cascaded convolutional networks. In: IEEE CVPR, pp. 5131\u20135139 (2017)","DOI":"10.1109\/CVPR.2017.545"},{"key":"13_CR16","unstructured":"Diba, A., Sharma, V., Stiefelhagen, R., Van Gool, L.: Object discovery by generative adversarial & ranking networks. arXiv preprint arXiv:1711.08174 (2017)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Dong, B., Huang, Z., Guo, Y., Wang, Q., Niu, Z., Zuo, W.: Boosting weakly supervised object detection via learning bounding box adjusters. In: IEEE ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00287"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Dong, L., Bin, H.J., Yali, L., Shengjin, W., Hsuan, Y.M.: Weakly supervised object localization with progressive domain adaptation. In: IEEE CVPR, pp. 3512\u20133520 (2016)","DOI":"10.1109\/CVPR.2016.382"},{"key":"13_CR19","unstructured":"Fang, W., Chang, L., Wei, K., Xiangyang, J., Jianbin, J., Qixiang, Y.: CMIL: continuation multiple instance learning for weakly supervised object detection. In: IEEE CVPR (2019)"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Gao, W., et al.: TS-CAM: token semantic coupled attention map for weakly supervised object localization. CoRR abs\/2103.14862 (2021)","DOI":"10.1109\/ICCV48922.2021.00288"},{"key":"13_CR21","unstructured":"Gao, Y., et al.: C-MIDN: coupled multiple instance detection network with segmentation guidance for weakly supervised object detection. In: IEEE ICCV (2019)"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Gudi, A., van Rosmalen, N., Loog, M., van Gemert, J.C.: Object-extent pooling for weakly supervised single-shot localization. In: BMVC (2017)","DOI":"10.5244\/C.31.36"},{"key":"13_CR23","unstructured":"Huang, Z., Zou, Y., Kumar, B.V.K.V., Huang, D.: Comprehensive attention self-distillation for weakly-supervised object detection. In: NeurIPS (2020)"},{"key":"13_CR24","unstructured":"Kantorov, V., et al.: Deep self-taught learning for weakly supervised object localization. In: IEEE CVPR, pp. 4294\u20134302 (2017)"},{"key":"13_CR25","doi-asserted-by":"publisher","unstructured":"ContextLocNet: context-aware deep network models for weakly supervised localization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 350\u2013365. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_22","DOI":"10.1007\/978-3-319-46454-1_22"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Kosugi, S., Yamasaki, T., Aizawa, K.: Object-aware instance labeling for weakly supervised object detection. In: IEEE ICCV (2019)","DOI":"10.1109\/ICCV.2019.00616"},{"key":"13_CR27","doi-asserted-by":"publisher","unstructured":"Zitnick, C.L., Doll\u00e1r, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 391\u2013405. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_26","DOI":"10.1007\/978-3-319-10602-1_26"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Li, X., Kan, M., Shan, S., Chen, X.: Weakly supervised object detection with segmentation collaboration. In: IEEE ICCV (2019)","DOI":"10.1109\/ICCV.2019.00983"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Mark, E., Luc, V.G., KI, W.C., John, W., Andrew, Z.: The pascal visual object classes (VOC) challenge. IJCV. 88(2), 303\u2013338 (2010)","DOI":"10.1007\/s11263-009-0275-4"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Meng, D., et al.: Conditional DETR for fast training convergence. In: IEEE ICCV, pp. 3651\u20133660, October 2021","DOI":"10.1109\/ICCV48922.2021.00363"},{"key":"13_CR31","unstructured":"Oh, S.H., Jae, L.Y., Stefanie, J., Trevor, D.: Weakly supervised discovery of visual pattern configurations. In: NeurIPS, pp. 1637\u20131645 (2014)"},{"key":"13_CR32","unstructured":"Oh, S.H., Ross, G., Stefanie, J., Julien, M., Zaid, H., Trevor, D.: On learning to localize objects with minimal supervision. In: ICML, pp. 1611\u20131619 (2014)"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Parthipan, S., Tao, X.: Weakly supervised object detector learning with model drift detection. In: IEEE ICCV, pp. 343\u2013350 (2011)","DOI":"10.1109\/ICCV.2011.6126261"},{"key":"13_CR34","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)"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Ren, Z., et al.: Instance-aware, context-focused, and memory-efficient weakly supervised object detection. In: IEEE CVPR, pp. 10595\u201310604 (2020)","DOI":"10.1109\/CVPR42600.2020.01061"},{"key":"13_CR36","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: IEEE CVPR, June 2019","DOI":"10.1109\/CVPR.2019.00075"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"RR, U.J., de Sande Koen EA, V., Theo, G., WM, S.A.: Selective search for object recognition. IJCV. 104(2), 154\u2013171 (2013)","DOI":"10.1007\/s11263-013-0620-5"},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Shen, Y., Ji, R., Chen, Z., Wu, Y., Huang, F.: UWSOD: toward fully-supervised-level capacity weakly supervised object detection. In: NeurIPS (2020)","DOI":"10.1109\/CVPR42600.2020.01134"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Shen, Y., Ji, R., Wang, C., Li, X., Li, X.: Weakly supervised object detection via object-specific pixel gradient. IEEE TNNLS 29(12), 5960\u20135970 (2018)","DOI":"10.1109\/TNNLS.2018.2816021"},{"key":"13_CR40","doi-asserted-by":"crossref","unstructured":"Shen, Y., Ji, R., Wang, Y., Wu, Y., Cao, L.: Cyclic guidance for weakly supervised joint detection and segmentation. In: IEEE CVPR, pp. 697\u2013707 (2019)","DOI":"10.1109\/CVPR.2019.00079"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Singh, K.K., Lee, Y.J.: You reap what you sow: using videos to generate high precision object proposals for weakly-supervised object detection. In: IEEE CVPR, pp. 9414\u20139422 (2019)","DOI":"10.1109\/CVPR.2019.00964"},{"key":"13_CR42","doi-asserted-by":"crossref","unstructured":"Tang, P., et al.: PCL: proposal cluster learning for weakly supervised object detection. IEEE TPAMI 42(1), 176\u2013191 (2020)","DOI":"10.1109\/TPAMI.2018.2876304"},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Tang, P., Wang, X., Bai, X., Liu, W.: Multiple instance detection network with online instance classifier refinement. In: IEEE CVPR, pp. 3059\u20133067 (2017)","DOI":"10.1109\/CVPR.2017.326"},{"key":"13_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1007\/978-3-030-01252-6_22","volume-title":"Computer Vision \u2013 ECCV 2018","author":"P Tang","year":"2018","unstructured":"Tang, P., et al.: Weakly supervised region proposal network and object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 370\u2013386. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_22"},{"issue":"3","key":"13_CR45","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s11263-012-0538-3","volume":"100","author":"D Thomas","year":"2012","unstructured":"Thomas, D., Bogdan, A., Vittorio, F.: Weakly supervised localization and learning with generic knowledge. IJCV 100(3), 275\u2013293 (2012)","journal-title":"IJCV"},{"key":"13_CR46","doi-asserted-by":"crossref","unstructured":"Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., J\u00e9gou, H.: Going deeper with image transformers. arXiv preprint arXiv:2103.17239 (2021)","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"13_CR47","unstructured":"Tsung-Yi, L., Priya, G., Ross, G., Kaiming, H., Doll\u00e1r, P.: Focal loss for dense object detection. In: IEEE ICCV (2017)"},{"key":"13_CR48","doi-asserted-by":"crossref","unstructured":"Wan, F., Wei, P., Jiao, J., Han, Z., Ye, Q.: Min-entropy latent model for weakly supervised object detection. In: IEEE CVPR, pp. 1297\u20131306 (2018)","DOI":"10.1109\/CVPR.2018.00141"},{"issue":"10","key":"13_CR49","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1109\/TPAMI.2019.2898858","volume":"41","author":"F Wan","year":"2019","unstructured":"Wan, F., Wei, P., Jiao, J., Han, Z., Ye, Q.: Min-entropy latent model for weakly supervised object detection. IEEE TPAMI 41(10), 2395\u20132409 (2019)","journal-title":"IEEE TPAMI"},{"key":"13_CR50","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1007\/978-3-030-01252-6_27","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Wei","year":"2018","unstructured":"Wei, Y., et al.: TS$$^{2}$$C: tight box mining with surrounding segmentation context for weakly supervised object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 454\u2013470. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_27"},{"key":"13_CR51","doi-asserted-by":"publisher","unstructured":"Ye, Q., Wan, F., Liu, C., Huang, Q., Ji, X.: Continuation multiple instance learning for weakly and fully supervised object detection. IEEE TNNLS, pp. 1\u201315 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2021.3070801","DOI":"10.1109\/TNNLS.2021.3070801"},{"key":"13_CR52","doi-asserted-by":"crossref","unstructured":"Ye, Q., Zhang, T., Qiu, Q., Zhang, B., Chen, J., Sapiro, G.: Self-learning scene-specific pedestrian detectors using a progressive latent model. In: IEEE CVPR, pp. 2057\u20132066 (2017)","DOI":"10.1109\/CVPR.2017.222"},{"key":"13_CR53","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Liu, B., Fu, J., Chao, H., Zhang, L.: WSOD2: learning bottom-up and top-down objectness distillation for weakly-supervised object detection. In: IEEE ICCV (2019)","DOI":"10.1109\/ICCV.2019.00838"},{"key":"13_CR54","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: IEEE CVPR, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"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\/978-3-031-20077-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T10:07:12Z","timestamp":1728295632000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20077-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200762","9783031200779"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20077-9_13","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":"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)"}}]}}