{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:53:46Z","timestamp":1761648826867,"version":"3.40.3"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030586034"},{"type":"electronic","value":"9783030586041"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58604-1_2","type":"book-chapter","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T22:02:49Z","timestamp":1604354569000},"page":"20-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["An Attention-Driven Two-Stage Clustering Method for Unsupervised Person Re-identification"],"prefix":"10.1007","author":[{"given":"Zilong","family":"Ji","sequence":"first","affiliation":[]},{"given":"Xiaolong","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Xiaohan","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Tiejun","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Si","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"2_CR1","unstructured":"Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027\u20131035. Society for Industrial and Applied Mathematics (2007)"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Chen, B., Deng, W., Hu, J.: Mixed high-order attention network for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00046"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Chen, G., Lin, C., Ren, L., Lu, J., Zhou, J.: Self-critical attention learning for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9637\u20139646 (2019)","DOI":"10.1109\/ICCV.2019.00973"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Dai, Z., Chen, M., Gu, X., Zhu, S., Tan, P.: Batch dropblock network for person re-identification and beyond. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3691\u20133701 (2019)","DOI":"10.1109\/ICCV.2019.00379"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., Jiao, J.: Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 994\u20131003 (2018)","DOI":"10.1109\/CVPR.2018.00110"},{"issue":"4","key":"2_CR6","first-page":"83","volume":"14","author":"H Fan","year":"2018","unstructured":"Fan, H., Zheng, L., Yan, C., Yang, Y.: Unsupervised person re-identification: clustering and fine-tuning. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 14(4), 83 (2018)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2360\u20132367. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539926"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Fu, Y., Wei, Y., Wang, G., Zhou, Y., Shi, H., Huang, T.S.: Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6112\u20136121 (2019)","DOI":"10.1109\/ICCV.2019.00621"},{"key":"2_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/978-3-540-88682-2_21","volume-title":"Computer Vision \u2013 ECCV 2008","author":"D Gray","year":"2008","unstructured":"Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 262\u2013275. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-88682-2_21"},{"key":"2_CR10","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2_CR12","unstructured":"Huang, H., et al.: EANet: Enhancing alignment for cross-domain person re-identification. arXiv preprint arXiv:1812.11369 (2018)"},{"key":"2_CR13","unstructured":"Kodirov, E., Xiang, T., Gong, S.: Dictionary learning with iterative laplacian regularisation for unsupervised person re-identification. In: BMVC, vol. 3, p. 8 (2015)"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Lan, X., Wang, H., Gong, S., Zhu, X.: Deep reinforcement learning attention selection for person re-identification. In: BMVC (2017)","DOI":"10.5244\/C.31.121"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, X., Zhang, Z., Huang, K.: Learning deep context-aware features over body and latent parts for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 384\u2013393 (2017)","DOI":"10.1109\/CVPR.2017.782"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Li, S., Bak, S., Carr, P., Wang, X.: Diversity regularized spatiotemporal attention for video-based person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 369\u2013378 (2018)","DOI":"10.1109\/CVPR.2018.00046"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Li, W., Zhu, X., Gong, S.: Harmonious attention network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2285\u20132294 (2018)","DOI":"10.1109\/CVPR.2018.00243"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Li, Y.J., Yang, F.E., Liu, Y.C., Yeh, Y.Y., Du, X., Frank Wang, Y.C.: Adaptation and re-identification network: an unsupervised deep transfer learning approach to person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 172\u2013178 (2018)","DOI":"10.1109\/CVPRW.2018.00054"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2015)","DOI":"10.1109\/CVPR.2015.7298832"},{"key":"2_CR20","unstructured":"Lin, S., Li, H., Li, C.T., Kot, A.C.: Multi-task mid-level feature alignment network for unsupervised cross-dataset person re-identification. arXiv preprint arXiv:1807.01440 (2018)"},{"key":"2_CR21","first-page":"8738","volume":"33","author":"Y Lin","year":"2019","unstructured":"Lin, Y., Dong, X., Zheng, L., Yan, Y., Yang, Y.: A bottom-up clustering approach to unsupervised person re-identification. Proc. AAAI Conf. Artif. Intell. 33, 8738\u20138745 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Liu, J.: Identity preserving generative adversarial network for cross-domain person re-identification. arXiv preprint arXiv:1811.11510 (2018)","DOI":"10.1109\/ACCESS.2019.2933910"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: HydraPlus-Net: attentive deep features for pedestrian analysis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 350\u2013359 (2017)","DOI":"10.1109\/ICCV.2017.46"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Peng, P., et al.: Unsupervised cross-dataset transfer learning for person re-identification. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)","DOI":"10.1109\/CVPR.2016.146"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Peng, P., et al.: Unsupervised cross-dataset transfer learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1306\u20131315 (2016)","DOI":"10.1109\/CVPR.2016.146"},{"key":"2_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-319-48881-3_2","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"E Ristani","year":"2016","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for\u00a0multi-target, multi-camera tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17\u201335. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_2"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Song, C., Huang, Y., Ouyang, W., Wang, L.: Mask-guided contrastive attention model for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1179\u20131188 (2018)","DOI":"10.1109\/CVPR.2018.00129"},{"key":"2_CR28","unstructured":"Song, L., et al.: Unsupervised domain adaptive re-identification: Theory and practice. arXiv preprint arXiv:1807.11334 (2018)"},{"key":"2_CR29","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-3-662-08968-2_16","volume-title":"New Directions in Statistical Physics","author":"M Steinbach","year":"2004","unstructured":"Steinbach, M., Ert\u00f6z, L., Kumar, V.: The challenges of clustering high dimensional data. In: Wille, L.T. (ed.) New Directions in Statistical Physics, pp. 273\u2013309. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-662-08968-2_16"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3960\u20133969 (2017)","DOI":"10.1109\/ICCV.2017.427"},{"key":"2_CR31","unstructured":"Wang, H., Fan, Y., Wang, Z., Jiao, L., Schiele, B.: Parameter-free spatial attention network for person re-identification. arXiv preprint arXiv:1811.12150 (2018)"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhu, X., Gong, S., Li, W.: Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2275\u20132284 (2018)","DOI":"10.1109\/CVPR.2018.00242"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer GAN to bridge domain gap for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 79\u201388 (2018)","DOI":"10.1109\/CVPR.2018.00016"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhang, S., Yao, H., Gao, W., Tian, Q.: GLAD: global-local-alignment descriptor for pedestrian retrieval. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 420\u2013428. ACM (2017)","DOI":"10.1145\/3123266.3123279"},{"key":"2_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"2_CR36","unstructured":"Xia, B.N., Gong, Y., Zhang, Y., Poellabauer, C.: Second-order non-local attention networks for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3760\u20133769 (2019)"},{"key":"2_CR37","unstructured":"Xiong, F., Xiao, Y., Cao, Z., Gong, K., Fang, Z., Zhou, J.T.: Towards good practices on building effective cnn baseline model for person re-identification. arXiv preprint arXiv:1807.11042 (2018)"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhao, R., Zhu, F., Wang, H., Ouyang, W.: Attention-aware compositional network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2119\u20132128 (2018)","DOI":"10.1109\/CVPR.2018.00226"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Yang, F., et al.: Asymmetric co-teaching for unsupervised cross-domain person re-identification. In: AAAI, pp. 12597\u201312604 (2020)","DOI":"10.1609\/aaai.v34i07.6950"},{"key":"2_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cao, J., Shen, C., You, M.: Self-training with progressive augmentation for unsupervised cross-domain person re-identification. arXiv preprint arXiv:1907.13315 (2019)","DOI":"10.1109\/ICCV.2019.00831"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Zhao, H., et al.: Spindle Net: person re-identification with human body region guided feature decomposition and fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1077\u20131085 (2017)","DOI":"10.1109\/CVPR.2017.103"},{"key":"2_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3586\u20133593 (2013)","DOI":"10.1109\/CVPR.2013.460"},{"key":"2_CR43","doi-asserted-by":"publisher","first-page":"4500","DOI":"10.1109\/TIP.2019.2910414","volume":"28","author":"L Zheng","year":"2019","unstructured":"Zheng, L., Huang, Y., Lu, H., Yang, Y.: Pose invariant embedding for deep person re-identification. IEEE Trans. Image Process. 28, 4500\u20134509 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR44","doi-asserted-by":"crossref","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: The IEEE International Conference on Computer Vision (ICCV) (December 2015)","DOI":"10.1109\/ICCV.2015.133"},{"key":"2_CR45","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1318\u20131327 (2017)","DOI":"10.1109\/CVPR.2017.389"},{"key":"2_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/978-3-030-01261-8_11","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Z Zhong","year":"2018","unstructured":"Zhong, Z., Zheng, L., Li, S., Yang, Y.: Generalizing a person retrieval model hetero- and homogeneously. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 176\u2013192. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_11"},{"issue":"3","key":"2_CR47","doi-asserted-by":"publisher","first-page":"1176","DOI":"10.1109\/TIP.2018.2874313","volume":"28","author":"Z Zhong","year":"2018","unstructured":"Zhong, Z., Zheng, L., Zheng, Z., Li, S., Yang, Y.: CamStyle: a novel data augmentation method for person re-identification. IEEE Trans. Image Process. 28(3), 1176\u20131190 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR48","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"2_CR49","doi-asserted-by":"crossref","unstructured":"Zhou, S., Wang, F., Huang, Z., Wang, J.: Discriminative feature learning with consistent attention regularization for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8040\u20138049 (2019)","DOI":"10.1109\/ICCV.2019.00813"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58604-1_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:07:08Z","timestamp":1730506028000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58604-1_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030586034","9783030586041"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58604-1_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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","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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}