{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T06:09:13Z","timestamp":1784268553078,"version":"3.55.0"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198380","type":"print"},{"value":"9783031198397","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\/978-3-031-19839-7_10","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T11:40:06Z","timestamp":1666438806000},"page":"161-178","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["TIPS: Text-Induced Pose Synthesis"],"prefix":"10.1007","author":[{"given":"Prasun","family":"Roy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subhankar","family":"Ghosh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saumik","family":"Bhattacharya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umapada","family":"Pal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Blumenstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: new benchmark and state of the art analysis. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)","DOI":"10.1109\/CVPR.2014.471"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Athiwaratkun, B., Wilson, A.G., Anandkumar, A.: Probabilistic FastText for multi-sense word embeddings. arXiv preprint arXiv:1806.02901 (2018)","DOI":"10.18653\/v1\/P18-1001"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Balakrishnan, G., Zhao, A., Dalca, A.V., Durand, F., Guttag, J.: Synthesizing images of humans in unseen poses. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00870"},{"key":"10_CR4","unstructured":"Briq, R., Kochar, P., Gall, J.: Towards better adversarial synthesis of human images from text. arXiv preprint arXiv:2107.01869 (2021)"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.143"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Chen, L., Maddox, R.K., Duan, Z., Xu, C.: Hierarchical cross-modal talking face generation with dynamic pixel-wise loss. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00802"},{"key":"10_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Analy. Mach. Intell. (TPAMI) 38, 295\u2013307 (2015)","journal-title":"IEEE Trans. Pattern Analy. Mach. Intell. (TPAMI)"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Esser, P., Sutter, E., Ommer, B.: A variational U-Net for conditional appearance and shape generation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00923"},{"key":"10_CR10","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: The Conference on Neural Information Processing Systems (NeurIPS) (2014)"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"G\u00fcler, R.A., Neverova, N., Kokkinos, I.: DensePose: dense human pose estimation in the wild. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00762"},{"key":"10_CR12","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs. arXiv preprint arXiv:1704.00028 (2017)"},{"key":"10_CR13","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":"10_CR14","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $$<$$0.5\u00a0MB model size. arXiv preprint arXiv:1602.07360 (2016)"},{"key":"10_CR15","unstructured":"Ioffe, S., Szegedy, C.: Batch Normalization: accelerating deep network training by reducing internal covariate shift. In: The International Conference on Machine Learning (ICML) (2015)"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-Image translation with conditional adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: The European Conference on Computer Vision (ECCV) (2016)","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"10_CR19","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: The International Conference on Learning Representations (ICLR) (2015)"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Lassner, C., Pons-Moll, G., Gehler, P.V.: A generative model of people in clothing. In: The IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.98"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"10_CR22","doi-asserted-by":"publisher","first-page":"9584","DOI":"10.1109\/TIP.2020.3029455","volume":"29","author":"K Li","year":"2020","unstructured":"Li, K., Zhang, J., Liu, Y., Lai, Y.K., Dai, Q.: PoNA: pose-guided non-local attention for human pose transfer. IEEE Trans. Image Process. (TIP) 29, 9584\u20139599 (2020)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, C., Loy, C.C.: Dense intrinsic appearance flow for human pose transfer. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00381"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Li, Y., Min, M., Shen, D., Carlson, D., Carin, L.: Video generation from text. In: The AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12233"},{"key":"10_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: DeepFashion: powering robust clothes recognition and retrieval with rich annotations. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.124"},{"key":"10_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2816795.2818013","volume":"34","author":"M Loper","year":"2015","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (TOG) 34, 1\u201316 (2015)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., Van Gool, L.: Pose guided person image generation. In: The Conference on Neural Information Processing Systems (NeurIPS) (2017)","DOI":"10.1109\/CVPR.2018.00018"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Ma, L., Sun, Q., Georgoulis, S., Van Gool, L., Schiele, B., Fritz, M.: Disentangled person image generation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00018"},{"key":"10_CR30","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"10_CR31","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"10_CR32","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve Restricted Boltzmann Machines. In: The International Conference on Machine Learning (ICML) (2010)"},{"key":"10_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1007\/978-3-030-01219-9_8","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Neverova","year":"2018","unstructured":"Neverova, N., Alp G\u00fcler, R., Kokkinos, I.: Dense pose transfer. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 128\u2013143. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_8"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Pumarola, A., Agudo, A., Sanfeliu, A., Moreno-Noguer, F.: Unsupervised person image synthesis in arbitrary poses. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00899"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Qiao, T., Zhang, J., Xu, D., Tao, D.: MirrorGAN: learning text-to-image generation by redescription. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00160"},{"key":"10_CR36","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: The International Conference on Learning Representations (ICLR) (2016)"},{"key":"10_CR37","unstructured":"Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. In: The International Conference on Machine Learning (ICML) (2016)"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Roy, P., Bhattacharya, S., Ghosh, S., Pal, U.: Multi-scale attention guided pose transfer. arXiv preprint arXiv:2202.06777 (2022)","DOI":"10.1016\/j.patcog.2023.109315"},{"key":"10_CR39","doi-asserted-by":"crossref","unstructured":"Roy, P., Ghosh, S., Bhattacharya, S., Pal, U., Blumenstein, M.: Scene aware person image generation through global contextual conditioning. In: The International Conference on Pattern Recognition (ICPR) (2022)","DOI":"10.1109\/ICPR56361.2022.9956682"},{"key":"10_CR40","unstructured":"Salimans, T., Goodfellow, I.J., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: The Conference on Neural Information Processing Systems (NeurIPS) (2016)"},{"key":"10_CR41","doi-asserted-by":"crossref","unstructured":"Sangkloy, P., Lu, J., Fang, C., Yu, F., Hays, J.: Scribbler: controlling deep image synthesis with sketch and color. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.723"},{"key":"10_CR42","doi-asserted-by":"crossref","unstructured":"Siarohin, A., Sangineto, E., Lathuili\u00e8re, S., Sebe, N.: Deformable GANs for pose-based human image generation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00359"},{"key":"10_CR43","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: The International Conference on Learning Representations (ICLR) (2015)"},{"key":"10_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/978-3-030-01261-8_36","volume-title":"Computer Vision \u2013 ECCV 2018","author":"B Wang","year":"2018","unstructured":"Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 607\u2013623. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_36"},{"key":"10_CR45","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. (TIP) 13, 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Yeh, R.A., Chen, C., Yian Lim, T., Schwing, A.G., Hasegawa-Johnson, M., Do, M.N.: Semantic image inpainting with deep generative models. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.728"},{"key":"10_CR47","doi-asserted-by":"crossref","unstructured":"Zanfir, M., Popa, A.I., Zanfir, A., Sminchisescu, C.: Human appearance transfer. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00565"},{"key":"10_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"10_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Briq, R., Tanke, J., Gall, J.: Adversarial synthesis of human pose from text. In: The DAGM German Conference on Pattern Recognition (GCPR) (2020)","DOI":"10.1007\/978-3-030-71278-5_11"},{"key":"10_CR50","doi-asserted-by":"crossref","unstructured":"Zhao, B., Wu, X., Cheng, Z.Q., Liu, H., Jie, Z., Feng, J.: Multi-view image generation from a single-view. In: The ACM International Conference on Multimedia (MM) (2018)","DOI":"10.1145\/3240508.3240536"},{"key":"10_CR51","doi-asserted-by":"publisher","first-page":"1898","DOI":"10.1109\/TIP.2020.3031108","volume":"30","author":"H Zheng","year":"2020","unstructured":"Zheng, H., Chen, L., Xu, C., Luo, J.: Pose flow learning from person images for pose guided synthesis. IEEE Trans. Image Process. (TIP) 30, 1898\u20131909 (2020)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"10_CR52","doi-asserted-by":"crossref","unstructured":"Zhou, X., Huang, S., Li, B., Li, Y., Li, J., Zhang, Z.: Text guided person image synthesis. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00378"},{"key":"10_CR53","first-page":"1","volume":"39","author":"Y Zhou","year":"2020","unstructured":"Zhou, Y., Han, X., Shechtman, E., Echevarria, J., Kalogerakis, E., Li, D.: MakeItTalk: speaker-aware talking-head animation. ACM Trans. Graph. (TOG) 39, 1\u201315 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"10_CR54","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: The IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"10_CR55","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Huang, T., Shi, B., Yu, M., Wang, B., Bai, X.: Progressive pose attention transfer for person image generation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00245"}],"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\/978-3-031-19839-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:19:30Z","timestamp":1709813970000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19839-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198380","9783031198397"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19839-7_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":"23 October 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)"}}]}}