{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T17:15:46Z","timestamp":1765041346672,"version":"3.40.3"},"publisher-location":"Cham","reference-count":77,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031197833"},{"type":"electronic","value":"9783031197840"}],"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\/978-3-031-19784-0_2","type":"book-chapter","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T14:02:50Z","timestamp":1667138570000},"page":"18-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["End-to-End Visual Editing with\u00a0a\u00a0Generatively Pre-trained Artist"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9556-2633","authenticated-orcid":false,"given":"Andrew","family":"Brown","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1377-3278","authenticated-orcid":false,"given":"Cheng-Yang","family":"Fu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8959-3284","authenticated-orcid":false,"given":"Omkar","family":"Parkhi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1272-3359","authenticated-orcid":false,"given":"Tamara L.","family":"Berg","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1374-2858","authenticated-orcid":false,"given":"Andrea","family":"Vedaldi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Abdal, R., Qin, Y., Wonka, P.: Image2stylegan: How to embed images into the stylegan latent space? In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00453"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Abdal, R., Qin, Y., Wonka, P.: Image2stylegan++: how to edit the embedded images? In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00832"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Abdal, R., Zhu, P., Femiani, J., Mitra, N.J., Wonka, P.: Clip2stylegan: unsupervised extraction of stylegan edit directions. arXiv:2112.05219 [cs.CV] (2021)","DOI":"10.1145\/3528233.3530747"},{"key":"2_CR4","unstructured":"Bau, D., et al.: Paint by word. arXiv:2103.10951 [cs.CV] (2021)"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Bau, D., et al.: Semantic photo manipulation with a generative image prior. ACM Trans, Graph (2019)","DOI":"10.1145\/3306346.3323023"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Bau, D., Zhu, J.Y., Strobelt, H., Lapedriza, A., Zhou, B., Torralba, A.: Understanding the role of individual units in a deep neural network. In: Proceedings of the National Academy of Sciences (2020)","DOI":"10.1073\/pnas.1907375117"},{"key":"2_CR7","unstructured":"Bau, D., et al.: Inverting layers of a large generator. In: ICLR 2019 Debugging Machine Learning Models Workshop (2019)"},{"key":"2_CR8","unstructured":"Chai, L., Wulff, J., Isola, P.: Using latent space regression to analyze and leverage compositionality in gans. In: ICLR (2021)"},{"key":"2_CR9","unstructured":"Chen, M., et al.: Generative pretraining from pixels. In: ICML (2020)"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00916"},{"key":"2_CR11","unstructured":"Crowson, K.: VQGAN-CLIP (2021). https:\/\/github.com\/nerdyrodent\/VQGAN-CLIP"},{"key":"2_CR12","unstructured":"Dai, B., Wipf, D.: Diagnosing and enhancing VAE models. In: ICLR (2019)"},{"key":"2_CR13","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: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2_CR14","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)"},{"key":"2_CR15","unstructured":"Ding, M., et al.: Cogview: mastering text-to-image generation via transformers. In: NeurIPS (2021)"},{"key":"2_CR16","unstructured":"Dolhansky, B., et al.: The deepfake detection challenge dataset (2020)"},{"key":"2_CR17","unstructured":"Esser, P., Rombach, R., Blattmann, A., Ommer, B.: Imagebart: bidirectional context with multinomial diffusion for autoregressive image synthesis. In: NeurIPS (2021)"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Esser, P., Rombach, R., Ommer, B.: A disentangling invertible interpretation network for explaining latent representations. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00924"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"2_CR20","unstructured":"Fauw, J.D., Dieleman, S., Simonyan, K.: Hierarchical autoregressive image models with auxiliary decoders. arXiv:1903.04933 [cs.CV] (2019)"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Gafni, O., Polyak, A., Ashual, O., Sheynin, S., Parikh, D., Taigman, Y.: Make-a-scene: scene-based text-to-image generation with human priors (2022)","DOI":"10.1007\/978-3-031-19784-0_6"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Galatolo., F., Cimino., M., Vaglini, G.: Generating images from caption and vice versa via clip-guided generative latent space search. In: Proceedings of the International Conference on Image Processing and Vision Engineering (2021)","DOI":"10.5220\/0010503701660174"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Ghosh, P., Zietlow, D., Black, M.J., Davis, L.S., Hu, X.: Invgan: invertible gans. arXiv:2112.04598 [cs.CV] (2021)","DOI":"10.1007\/978-3-031-16788-1_1"},{"key":"2_CR24","unstructured":"Goyal, A., Lamb, A., Zhang, Y., Zhang, S., Courville, A., Bengio, Y.: Professor forcing: a new algorithm for training recurrent networks. In: NeurIPS (2016)"},{"key":"2_CR25","unstructured":"Guan, S., Tai, Y., Ni, B., Zhu, F., Huang, F., Yang, X.: Collaborative learning for faster stylegan embedding. arXiv:2007.01758 [cs.CV] (2020)"},{"key":"2_CR26","unstructured":"H\u00e4rk\u00f6nen, E., Hertzmann, A., Lehtinen, J., Paris, S.: Ganspace: discovering interpretable gan controls. arXiv:2004.02546 [cs.CV] (2020)"},{"key":"2_CR27","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS (2017)"},{"key":"2_CR28","unstructured":"Holtzman, A., Buys, J., Du, L., Forbes, M., Choi, Y.: The curious case of neural text degeneration. In: ICLR (2020)"},{"issue":"4","key":"2_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073659","volume":"36","author":"S Iizuka","year":"2017","unstructured":"Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. 36(4), 1\u201314 (2017)","journal-title":"ACM Trans. Graph."},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Isola, P., Liu, C.: Scene collaging: analysis and synthesis of natural images with semantic layers. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.457"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"2_CR32","unstructured":"Issenhuth, T., Tanielian, U., Mary, J., Picard, D.: Edibert, a generative model for image editing. arXiv:2111.15264 [cs.CV] (2021)"},{"key":"2_CR33","unstructured":"Jahanian, A., Chai, L., Isola, P.: On the \"steerability\" of generative adversarial networks. In: ICLR (2020)"},{"key":"2_CR34","unstructured":"Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. In: NeurIPS (2020)"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"2_CR36","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: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"2_CR37","doi-asserted-by":"crossref","unstructured":"Kim, H., Choi, Y., Kim, J., Yoo, S., Uh, Y.: Exploiting spatial dimensions of latent in gan for real-time image editing. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00091"},{"key":"2_CR38","unstructured":"Lipton, Z.C., Tripathi, S.: Precise recovery of latent vectors from generative adversarial networks. arXiv:1702.04782 [cs.LG] (2017)"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Liu, G., Reda, F.A., Shih, K.J., Wang, T.C., Tao, A., Catanzaro, B.: Image inpainting for irregular holes using partial convolutions. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01252-6_6"},{"key":"2_CR40","unstructured":"Liu, X., et al.: More control for free! image synthesis with semantic diffusion guidance. arXiv:2112.05744 [cs.CV] (2021)"},{"key":"2_CR41","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2019)"},{"key":"2_CR42","unstructured":"Mokady, R., Benaim, S., Wolf, L., Bermano, A.: Mask based unsupervised content transfer. arXiv:1906.06558 [cs.CV] (2018)"},{"key":"2_CR43","unstructured":"Nichol, A., et al.: Glide: towards photorealistic image generation and editing with text-guided diffusion models (2021)"},{"key":"2_CR44","unstructured":"van den Oord, A., Vinyals, O., Kavukcuoglu, K.: Neural discrete representation learning. arXiv:1711.00937 [cs.LG] (2017)"},{"key":"2_CR45","doi-asserted-by":"crossref","unstructured":"Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00244"},{"key":"2_CR46","doi-asserted-by":"crossref","unstructured":"Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., Lischinski, D.: Styleclip: Text-driven manipulation of stylegan imagery. arXiv:2103.17249 [cs.CV] (2021)","DOI":"10.1109\/ICCV48922.2021.00209"},{"key":"2_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/978-3-030-58539-6_35","volume-title":"Computer Vision \u2013 ECCV 2020","author":"W Peebles","year":"2020","unstructured":"Peebles, W., Peebles, J., Zhu, J.-Y., Efros, A., Torralba, A.: The hessian penalty: a weak prior for unsupervised disentanglement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 581\u2013597. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58539-6_35"},{"key":"2_CR48","unstructured":"Press, O., Galanti, T., Benaim, S., Wolf, L.: Emerging disentanglement in auto-encoder based unsupervised image content transfer. In: ICLR (2019)"},{"key":"2_CR49","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 [cs.LG] (2016)"},{"issue":"8","key":"2_CR50","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)","journal-title":"OpenAI blog"},{"key":"2_CR51","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents (2022)"},{"key":"2_CR52","unstructured":"Ramesh, A., et al.: Zero-shot text-to-image generation. In: ICML (2021)"},{"key":"2_CR53","unstructured":"Razavi, A., van den Oord, A., Vinyals, O.: Generating diverse high-fidelity images with vq-vae-2. In: NeurIPS (2019)"},{"key":"2_CR54","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. arXiv:1606.03498 [cs.LG] (2016)"},{"key":"2_CR55","doi-asserted-by":"crossref","unstructured":"Schaldenbrand, P., Liu, Z., Oh, J.: Styleclipdraw: Coupling content and style in text-to-drawing synthesis. arXiv:2111.03133 [cs.CV] (2021)","DOI":"10.24963\/ijcai.2022\/688"},{"key":"2_CR56","doi-asserted-by":"crossref","unstructured":"Schwettmann, S., Hernandez, E., Bau, D., Klein, S., Andreas, J., Torralba, A.: Toward a visual concept vocabulary for gan latent space. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00673"},{"key":"2_CR57","doi-asserted-by":"crossref","unstructured":"Shen, Y., Gu, J., Tang, X., Zhou, B.: Interpreting the latent space of gans for semantic face editing. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00926"},{"key":"2_CR58","doi-asserted-by":"crossref","unstructured":"Shi, J., Xu, N., Zheng, H., Smith, A., Luo, J., Xu, C.: Spaceedit: learning a unified editing space for open-domain image editing. arXiv:2112.00180 [cs.CV] (2021)","DOI":"10.1109\/CVPR52688.2022.01911"},{"key":"2_CR59","doi-asserted-by":"crossref","unstructured":"Shocher, A. et al.: Semantic pyramid for image generation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00748"},{"key":"2_CR60","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"2_CR61","doi-asserted-by":"crossref","unstructured":"Tov, O., Alaluf, Y., Nitzan, Y., Patashnik, O., Cohen-Or, D.: Designing an encoder for stylegan image manipulation. arXiv:2102.02766 [cs.CV] (2021)","DOI":"10.1145\/3476576.3476706"},{"key":"2_CR62","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H., Shen, X., Lin, Z.L., Sunkavalli, K., Lu, X., Yang, M.H.: Deep image harmonization. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.299"},{"key":"2_CR63","unstructured":"Voynov, A., Babenko, A.: Unsupervised discovery of interpretable directions in the gan latent space. In: ICML (2020)"},{"key":"2_CR64","doi-asserted-by":"crossref","unstructured":"Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional gans. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00917"},{"issue":"2","key":"2_CR65","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","volume":"1","author":"RJ Williams","year":"1989","unstructured":"Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270\u2013280 (1989)","journal-title":"Neural Comput."},{"key":"2_CR66","doi-asserted-by":"crossref","unstructured":"Wu, C., et al.: N$$\\backslash $$\" uwa: visual synthesis pre-training for neural visual world creation. arXiv:2111.12417 [cs.CV] (2021)","DOI":"10.1007\/978-3-031-19787-1_41"},{"key":"2_CR67","doi-asserted-by":"crossref","unstructured":"Wu, Z., Lischinski, D., Shechtman, E.: Stylespace analysis: Disentangled controls for stylegan image generation. arXiv:2011.12799 [cs.CV] (2020)","DOI":"10.1109\/CVPR46437.2021.01267"},{"key":"2_CR68","doi-asserted-by":"crossref","unstructured":"Xia, W., Yang, Y., Xue, J.H., Wu, B.: Tedigan: text-guided diverse face image generation and manipulation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00229"},{"key":"2_CR69","unstructured":"Xiao, Z., Yan, Q., Chen, Y.A., Amit, Y.: Generative latent flow. arXiv:1905.10485 [cs.CV] (2019)"},{"key":"2_CR70","doi-asserted-by":"crossref","unstructured":"Xu, Y., Shen, Y., Zhu, J., Yang, C., Zhou, B.: Generative hierarchical features from synthesizing images. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00441"},{"issue":"5","key":"2_CR71","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1007\/s11263-020-01429-5","volume":"129","author":"C Yang","year":"2021","unstructured":"Yang, C., Shen, Y., Zhou, B.: Semantic hierarchy emerges in deep generative representations for scene synthesis. Int. J. Comput. Vis. 129(5), 1451\u20131466 (2021). https:\/\/doi.org\/10.1007\/s11263-020-01429-5","journal-title":"Int. J. Comput. Vis."},{"key":"2_CR72","unstructured":"Yu, F., Zhang, Y., Song, S., Seff, A., Xiao, J.: Lsun: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv:1506.03365 [cs.CV] (2015)"},{"key":"2_CR73","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: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"2_CR74","unstructured":"Zhang, Z., et al.: UFC-BERT: unifying multi-modal controls for conditional image synthesis. In: NeurIPS (2021)"},{"key":"2_CR75","unstructured":"Zhao, S., et al.: Large scale image completion via co-modulated generative adversarial networks. In: ICLR (2021)"},{"key":"2_CR76","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1007\/978-3-030-58520-4_35","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Shen, Y., Zhao, D., Zhou, B.: In-domain gan inversion for real image editing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 592\u2013608. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_35"},{"key":"2_CR77","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-46454-1_36","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J-Y Zhu","year":"2016","unstructured":"Zhu, J.-Y., Kr\u00e4henb\u00fchl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 597\u2013613. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_36"}],"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\/978-3-031-19784-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:29:23Z","timestamp":1710358163000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19784-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197833","9783031197840"],"references-count":77,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19784-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"31 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)"}}]}}