{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T03:44:30Z","timestamp":1783568670279,"version":"3.55.0"},"publisher-location":"Cham","reference-count":66,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197963","type":"print"},{"value":"9783031197970","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\/978-3-031-19797-0_33","type":"book-chapter","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:28:41Z","timestamp":1667420921000},"page":"574-591","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":101,"title":["Efficient and\u00a0Degradation-Adaptive Network for\u00a0Real-World Image Super-Resolution"],"prefix":"10.1007","author":[{"given":"Jie","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"33_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual Losses for Real-Time Style Transfer and Super-Resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"33_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-11021-5_5","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"X Wang","year":"2019","unstructured":"Wang, X., et al.: ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. In: Leal-Taix\u00e9, Laura, Roth, Stefan (eds.) ECCV 2018. LNCS, vol. 11133, pp. 63\u201379. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11021-5_5"},{"key":"33_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-01234-2_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294\u2013310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18"},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Ma, C., Rao, Y., Cheng, Y., Chen, C., Lu, J., Zhou, J.: Structure-preserving super resolution with gradient guidance. In CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00779"},{"issue":"6","key":"33_CR5","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TIP.2010.2095871","volume":"20","author":"J Sun","year":"2010","unstructured":"Sun, J., Zongben, X., Shum, H.-Y.: Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans. Image Process. 20(6), 1529\u20131542 (2010)","journal-title":"IEEE Trans. Image Process."},{"key":"33_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-319-10593-2_13","volume-title":"Computer Vision \u2013 ECCV 2014","author":"C Dong","year":"2014","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184\u2013199. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_13"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S.M., Scholkopf, B., Hirsch, M.: EnhanceNet: single image super-resolution through automated texture synthesis. In: ICCV, (2017)","DOI":"10.1109\/ICCV.2017.481"},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution, In : CVPR (2016)","DOI":"10.1109\/CVPR.2016.181"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Soh, J.W., Park, G.Y., Jo, J., Cho, N.I.: Natural and realistic single image super-resolution with explicit natural manifold discrimination, In: CVPR (2019)D","DOI":"10.1109\/CVPR.2019.00831"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Jo, Y., Oh, S.W., Vajda, P., Kim, S.J.: Tackling the ill-posedness of super-resolution through adaptive target generation, In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01597"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Liu, A., Liu, Y., Gu, J., Qiao, Y., Dong. C.: Blind image super-resolution: a survey and beyond. arXiv preprint arXiv:2107.03055 (2021)","DOI":"10.1109\/TPAMI.2022.3203009"},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, J., Liang, K., Van Gool, L., Timofte, R.: Designing a practical degradation model for deep blind image super-resolution. In : ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00475"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00344"},{"key":"33_CR15","unstructured":"Luo, Z., Huang, Y., Li, S., Wang, L., Tan, T.: Unfolding the alternating optimization for blind super resolution. In: NeurIPS (2020)"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Gu, J., Lu, H., Zuo, W., Dong, C.: Blind super-resolution with iterative kernel correction. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00170"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Zhou, R., Susstrunk. Kernel modeling super-resolution on real low-resolution images. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00252"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, N., Van Gool, U.C.: DSLR-quality photos on mobile devices with deep convolutional networks. In ICCV (2017)","DOI":"10.1109\/ICCV.2017.355"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Cai, J., Zeng, H., Yong, H., Cao, Z., Zhang, L.: Toward real-world single image super-resolution: a new benchmark and a new model. In ICCV (2019)","DOI":"10.1109\/ICCV.2019.00318"},{"key":"33_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/978-3-030-58598-3_7","volume-title":"Computer Vision \u2013 ECCV 2020","author":"P Wei","year":"2020","unstructured":"Wei, P., et al.: Component divide-and-conquer for real-world image super-resolution. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 101\u2013117. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_7"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Danelljan, M., Timofte, R.: NTIRE 2020 challenge on real-world image super-resolution: methods and results. In: CVPRW (2020)","DOI":"10.1109\/CVPRW50498.2020.00255"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., et al:. AIM 2019 challenge on real-world image super-resolution: methods and results. In ICCVW (2019)","DOI":"10.1109\/ICCVW.2019.00442"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Fritsche, M., Gu, S., Timofte, R.: Frequency separation for real-world super-resolution. In: ICCVW (2019)","DOI":"10.1109\/ICCVW.2019.00445"},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Danelljan, M., Timofte, R.: Unsupervised learning for real-world super-resolution. In ICCVW (2019)D","DOI":"10.1109\/ICCVW.2019.00423"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Ji, X., Cao, Y., Tai, Y., Wang, C., Li, J., Huang, F.: Real-world super-resolution via kernel estimation and noise injection. In:: CVPRW (2020","DOI":"10.1109\/CVPRW50498.2020.00241"},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Ji, X., Cao, Y., Tai, Y., Wang, C., Li, J., Huang, F.: Real-world super-resolution via kernel estimation and noise injection. In: CVPRW (2020)","DOI":"10.1109\/CVPRW50498.2020.00241"},{"key":"33_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-030-01231-1_12","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Adrian Bulat","year":"2018","unstructured":"Bulat, Adrian, Yang, Jing, Tzimiropoulos, Georgios: To learn image super-resolution, use a gan to learn how to do image degradation first. In: Ferrari, Vittorio, Hebert, Martial, Sminchisescu, Cristian, Weiss, Yair (eds.) ECCV 2018. LNCS, vol. 11210, pp. 187\u2013202. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_12"},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Wei, Y., Gu, S., Li, Y., Timofte, R., Jin, L., Song, H.: Unsupervised real-world image super resolution via domain-distance aware training. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01318"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., Shan, Y.: Real-ESRGAN: training real-world blind super-resolution with pure synthetic data. In: ICCVW (2021)","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"33_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-46475-6_25","volume-title":"Computer Vision \u2013 ECCV 2016","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391\u2013407. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_25"},{"key":"33_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Zhang, L.: Edge-oriented convolution block for real-time super resolution on mobile devices. In: ACM Multimedia (2021)","DOI":"10.1145\/3474085.3475291"},{"key":"33_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1007\/978-3-030-01249-6_16","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Ahn","year":"2018","unstructured":"Ahn, N., Kang, B., Sohn, K.-A.: Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 256\u2013272. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_16"},{"issue":"4","key":"33_CR33","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1109\/LSP.2018.2890770","volume":"26","author":"W Yang","year":"2019","unstructured":"Yang, W., Wang, W., Zhang, X., Sun, S., Liao, Q.: Lightweight feature fusion network for single image super-resolution. IEEE Signal Process. Lett. 26(4), 538\u2013542 (2019)","journal-title":"IEEE Signal Process. Lett."},{"key":"33_CR34","doi-asserted-by":"crossref","unstructured":"Song, D., Wang, Y., Chen, H., Chang, X., Chunjing, X., Tao, D.: AdderSR: towards energy efficient image super-resolution. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01539"},{"key":"33_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chen, Q., Ng, R., Koltun, V.: Zoom to learn, learn to zoom. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00388"},{"key":"33_CR36","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SwinIR: image restoration using swin transformer. In: ICCVW (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"33_CR37","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"33_CR38","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"33_CR39","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Dong, C., Loy, C.C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00070"},{"key":"33_CR40","doi-asserted-by":"crossref","unstructured":"Fuoli, D., Van Gool, L., Timofte, R.: Fourier space losses for efficient perceptual image super-resolution. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00236"},{"key":"33_CR41","doi-asserted-by":"crossref","unstructured":"Xu, Y-S., Roy Tseng, S.-Y., Tseng, Y., Kuo, H.-K., Tsai, Y.M Unified dynamic convolutional network for super-resolution with variational degradations In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01251"},{"key":"33_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, K., Van Gool, L., Timofte, R.: Deep unfolding network for image super-resolution. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00328"},{"key":"33_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Deep plug-and-play super-resolution for arbitrary blur kernels. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00177"},{"key":"33_CR44","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Unsupervised degradation representation learning for blind super-resolution. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01044"},{"key":"33_CR45","doi-asserted-by":"crossref","unstructured":"Hui, Z., Li, J., Wang, X., Gao, X.: Learning the non-differentiable optimization for blind super-resolution. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00213"},{"key":"33_CR46","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2020.07.122","volume":"417","author":"P Liu","year":"2020","unstructured":"Liu, P., Zhang, H., Cao, Y., Liu, S., Ren, D., Zuo, W.: Learning cascaded convolutional networks for blind single image super-resolution. Neurocomputing 417, 371\u2013383 (2020)","journal-title":"Neurocomputing"},{"key":"33_CR47","doi-asserted-by":"crossref","unstructured":"Maeda, S.: Unpaired image super-resolution using pseudo-supervision. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00037"},{"key":"33_CR48","doi-asserted-by":"crossref","unstructured":"Yuan, Y., et al.: Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. In: CVPRW (2018)","DOI":"10.1109\/CVPRW.2018.00113"},{"key":"33_CR49","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: NeurIPS (2014)"},{"issue":"6","key":"33_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3355089.3356575","volume":"38","author":"V Cornillere","year":"2019","unstructured":"Cornillere, V., Djelouah, A., Yifan, W., Sorkine-Hornung, O., Schroers, C.: Blind image super-resolution with spatially variant degradations. ACM Trans. Graph. 38(6), 1\u201313 (2019)","journal-title":"ACM Trans. Graph."},{"key":"33_CR51","doi-asserted-by":"crossref","unstructured":"Kim, S.Y., Sim, H., Kim, M.: KOALAnet: Blind super-resolution using kernel-oriented adaptive local adjustment. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01047"},{"key":"33_CR52","doi-asserted-by":"crossref","unstructured":"Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Comput. 3(1), 79\u201387 (1991)","DOI":"10.1162\/neco.1991.3.1.79"},{"key":"33_CR53","doi-asserted-by":"crossref","unstructured":"Jordan, M.I., Jacobs, R.J.: Hierarchical mixtures of experts and the em algorithm. Neural Comput. 6(2), 181\u2013214 (1994)","DOI":"10.1162\/neco.1994.6.2.181"},{"key":"33_CR54","doi-asserted-by":"crossref","unstructured":"Gross, S., Ranzato, M.A., Szlam, A.: Hard mixtures of experts for large scale weakly supervised vision. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.540"},{"key":"33_CR55","doi-asserted-by":"crossref","unstructured":"Aljundi, R., Chakravarty, P., Tuytelaars, T.: Expert gate: lifelong learning with a network of experts. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.753"},{"key":"33_CR56","doi-asserted-by":"crossref","unstructured":"Maeda, S.: Fast and flexible image blind denoising via competition of experts. In: CVPRW (2020)","DOI":"10.1109\/CVPRW50498.2020.00272"},{"key":"33_CR57","doi-asserted-by":"publisher","first-page":"107169","DOI":"10.1016\/j.patcog.2019.107169","volume":"102","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Wang, L., Wang, H., Li, P., Huchuan, L.: Blind single image super-resolution with a mixture of deep networks. Pattern Recogn. 102, 107169 (2020)","journal-title":"Pattern Recogn."},{"key":"33_CR58","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Dynamic convolution: attention over convolution kernels. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"33_CR59","unstructured":"Li, Y., et al.: Revisiting dynamic convolution via matrix decomposition. In: ICLR (2021)"},{"key":"33_CR60","unstructured":"Li, C., Zhou, A., Yao, A.: Omni-dimensional dynamic convolution. In: ICLR (2021)"},{"key":"33_CR61","unstructured":"Yang, B., Bender, G., Le, Q.V., Ngiam, J.: CondConv: conditionally parameterized convolutions for efficient inference. In: NeurIPS (2019)"},{"key":"33_CR62","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: CVPRW (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"33_CR63","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"33_CR64","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-030-67070-2_1","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"K Zhang","year":"2020","unstructured":"Zhang, K., et al.: AIM 2020 challenge on efficient super-resolution: methods and results. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12537, pp. 5\u201340. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-67070-2_1"},{"key":"33_CR65","doi-asserted-by":"crossref","unstructured":"Zhang, K., et al.: Aim 2019 challenge on constrained super-resolution: methods and results. In: ICCVW (2019)","DOI":"10.1109\/ICCVW.2019.00441"},{"key":"33_CR66","doi-asserted-by":"crossref","unstructured":"Emad, M., Peemen, M., Corporaal, H.: MoESR: blind super-resolution using kernel-aware mixture of experts. In: WACV (2022)","DOI":"10.1109\/WACV51458.2022.00406"}],"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\/978-3-031-19797-0_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:46:11Z","timestamp":1667421971000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19797-0_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197963","9783031197970"],"references-count":66,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19797-0_33","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":"3 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)"}}]}}