{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:11:40Z","timestamp":1771330300566,"version":"3.50.1"},"publisher-location":"Cham","reference-count":59,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031728969","type":"print"},{"value":"9783031728976","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-72897-6_5","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T21:36:53Z","timestamp":1733089013000},"page":"70-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["GRIDS: Grouped Multiple-Degradation Restoration with\u00a0Image Degradation Similarity"],"prefix":"10.1007","author":[{"given":"Shuo","family":"Cao","sequence":"first","affiliation":[]},{"given":"Yihao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Wenlong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Dong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126\u2013135 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Bragman, F.J., Tanno, R., Ourselin, S., Alexander, D.C., Cardoso, J.: Stochastic filter groups for multi-task CNNs: learning specialist and generalist convolution kernels. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1385\u20131394 (2019)","DOI":"10.1109\/ICCV.2019.00147"},{"key":"5_CR3","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: Proceedings of the IEEE International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00318"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Masked image training for generalizable deep image denoising. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1692\u20131703 (2023)","DOI":"10.1109\/CVPR52729.2023.00169"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., Qiao, Y., Dong, C.: Activating more pixels in image super-resolution transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22367\u201322377 (2023)","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"5_CR6","first-page":"2039","volume":"33","author":"Z Chen","year":"2020","unstructured":"Chen, Z., et al.: Just pick a sign: optimizing deep multitask models with gradient sign dropout. Adv. Neural. Inf. Process. Syst. 33, 2039\u20132050 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, Y., Gu, J., Kong, L., Yang, X., Yu, F.: Dual aggregation transformer for image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12312\u201312321 (2023)","DOI":"10.1109\/ICCV51070.2023.01131"},{"key":"5_CR8","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":"5_CR9","first-page":"27503","volume":"34","author":"C Fifty","year":"2021","unstructured":"Fifty, C., Amid, E., Zhao, Z., Yu, T., Anil, R., Finn, C.: Efficiently identifying task groupings for multi-task learning. Adv. Neural. Inf. Process. Syst. 34, 27503\u201327516 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Gao, Y., Ma, J., Zhao, M., Liu, W., Yuille, A.L.: NDDR-CNN: layerwise feature fusing in multi-task CNNs by neural discriminative dimensionality reduction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3205\u20133214 (2019)","DOI":"10.1109\/CVPR.2019.00332"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Gu, S., Li, Y., Gool, L.V., Timofte, R.: Self-guided network for fast image denoising. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00260"},{"key":"5_CR12","unstructured":"Guangyuan, S., Li, Q., Zhang, W., Chen, J., Wu, X.M.: Recon: reducing conflicting gradients from the root for multi-task learning. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Guo, M., Haque, A., Huang, D.A., Yeung, S., Fei-Fei, L.: Dynamic task prioritization for multitask learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 270\u2013287 (2018)","DOI":"10.1007\/978-3-030-01270-0_17"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Kokkinos, I.: Ubernet: training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6129\u20136138 (2017)","DOI":"10.1109\/CVPR.2017.579"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: Deblurgan-v2: deblurring (orders-of-magnitude) faster and better. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8878\u20138887 (2019)","DOI":"10.1109\/ICCV.2019.00897"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Ledig, C., et\u00a0al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17452\u201317462 (2022)","DOI":"10.1109\/CVPR52688.2022.01693"},{"key":"5_CR19","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1007\/978-3-031-19797-0_42","volume-title":"European Conference on Computer Vision","author":"D Li","year":"2022","unstructured":"Li, D., Zhang, Y., Cheung, K.C., Wang, X., Qin, H., Li, H.: Learning degradation representations for image deblurring. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13678, pp. 736\u2013753. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19797-0_42"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Li, W., Lin, Z., Zhou, K., Qi, L., Wang, Y., Jia, J.: MAT: mask-aware transformer for large hole image inpainting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10758\u201310768 (2022)","DOI":"10.1109\/CVPR52688.2022.01049"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van\u00a0Gool, L., Timofte, R.: Swinir: image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"5_CR22","first-page":"18878","volume":"34","author":"B Liu","year":"2021","unstructured":"Liu, B., Liu, X., Jin, X., Stone, P., Liu, Q.: Conflict-averse gradient descent for multi-task learning. Adv. Neural. Inf. Process. Syst. 34, 18878\u201318890 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR23","unstructured":"Liu, Y., et al.: Unifying image processing as visual prompting question answering. arXiv preprint arXiv:2310.10513 (2023)"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Y., He, J., Gu, J., Kong, X., Qiao, Y., Dong, C.: Degae: a new pretraining paradigm for low-level vision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23292\u201323303 (2023)","DOI":"10.1109\/CVPR52729.2023.02231"},{"key":"5_CR25","unstructured":"Liu, Y., et al.: Discovering distinctive \u201csemantics\u201d in super-resolution networks. arXiv preprint arXiv:2108.00406 (2021)"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhao, H., Gu, J., Qiao, Y., Dong, C.: Evaluating the generalization ability of super-resolution networks. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3312313"},{"key":"5_CR27","unstructured":"Long, M., Cao, Z., Wang, J., Yu, P.S.: Learning multiple tasks with multilinear relationship networks. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"5_CR28","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"5_CR29","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vision"},{"key":"5_CR30","unstructured":"Ma, J., Cheng, T., Wang, G., Zhang, Q., Wang, X., Zhang, L.: Prores: exploring degradation-aware visual prompt for universal image restoration. arXiv preprint arXiv:2306.13653 (2023)"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: Cross-stitch networks for multi-task learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3994\u20134003 (2016)","DOI":"10.1109\/CVPR.2016.433"},{"key":"5_CR32","unstructured":"Potlapalli, V., Zamir, S.W., Khan, S., Khan, F.S.: Promptir: prompting for all-in-one blind image restoration. arXiv preprint arXiv:2306.13090 (2023)"},{"key":"5_CR33","unstructured":"Ruder, S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Ruder, S., Bingel, J., Augenstein, I., S\u00f8gaard, A.: Latent multi-task architecture learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 4822\u20134829 (2019)","DOI":"10.1609\/aaai.v33i01.33014822"},{"key":"5_CR35","unstructured":"Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"5_CR36","first-page":"21031","volume":"34","author":"J Shen","year":"2021","unstructured":"Shen, J., Zhen, X., Worring, M., Shao, L.: Variational multi-task learning with gumbel-softmax priors. Adv. Neural. Inf. Process. Syst. 34, 21031\u201321042 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR37","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"5_CR38","unstructured":"Standley, T., Zamir, A., Chen, D., Guibas, L., Malik, J., Savarese, S.: Which tasks should be learned together in multi-task learning? In: International Conference on Machine Learning, pp. 9120\u20139132. PMLR (2020)"},{"key":"5_CR39","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8174\u20138182 (2018)","DOI":"10.1109\/CVPR.2018.00853"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Wang, J., Yuan, C., Li, B., Deng, Y., Hu, W., Maybank, S.: Self-prior guided pixel adversarial networks for blind image inpainting. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3284431"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, Y., Dong, X., Xu, Q., Yang, J., An, W., Guo, Y.: Unsupervised degradation representation learning for blind super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10581\u201310590 (2021)","DOI":"10.1109\/CVPR46437.2021.01044"},{"key":"5_CR42","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: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 1905\u20131914 (2021)","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"5_CR43","unstructured":"Wang, Y., Tao, X., Qi, X., Shen, X., Jia, J.: Image inpainting via generative multi-column convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"5_CR44","doi-asserted-by":"crossref","unstructured":"Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: a general U-shaped transformer for image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17683\u201317693 (2022)","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"5_CR45","first-page":"5824","volume":"33","author":"T Yu","year":"2020","unstructured":"Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C.: Gradient surgery for multi-task learning. Adv. Neural. Inf. Process. Syst. 33, 5824\u20135836 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR46","doi-asserted-by":"crossref","unstructured":"Zamir, A.R., Sax, A., Shen, W., Guibas, L.J., Malik, J., Savarese, S.: Taskonomy: disentangling task transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3712\u20133722 (2018)","DOI":"10.1109\/CVPR.2018.00391"},{"key":"5_CR47","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5728\u20135739 (2022)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"5_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhu, Y., Yan, Q., Sun, J., Zhang, Y.: All-in-one multi-degradation image restoration network via hierarchical degradation representation. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 2285\u20132293 (2023)","DOI":"10.1145\/3581783.3611825"},{"key":"5_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Ingredient-oriented multi-degradation learning for image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5825\u20135835 (2023)","DOI":"10.1109\/CVPR52729.2023.00564"},{"key":"5_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Ingredient-oriented multi-degradation learning for image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5825\u20135835 (2023)","DOI":"10.1109\/CVPR52729.2023.00564"},{"key":"5_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liang, J., Van\u00a0Gool, L., Timofte, R.: Designing a practical degradation model for deep blind image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4791\u20134800 (2021)","DOI":"10.1109\/ICCV48922.2021.00475"},{"issue":"7","key":"5_CR52","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"9","key":"5_CR53","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"5_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, K., et al.: Deblurring by realistic blurring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2737\u20132746 (2020)","DOI":"10.1109\/CVPR42600.2020.00281"},{"key":"5_CR55","unstructured":"Zhang, W., et al.: Real-world image super-resolution as multi-task learning. In: Thirty-Seventh Conference on Neural Information Processing Systems (2023)"},{"key":"5_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, W., Liu, Y., Dong, C., Qiao, Y.: Ranksrgan: generative adversarial networks with ranker for image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3096\u20133105 (2019)","DOI":"10.1109\/ICCV.2019.00319"},{"key":"5_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, W., Shi, G., Liu, Y., Dong, C., Wu, X.M.: A closer look at blind super-resolution: degradation models, baselines, and performance upper bounds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 527\u2013536 (2022)","DOI":"10.1109\/CVPRW56347.2022.00068"},{"issue":"12","key":"5_CR58","doi-asserted-by":"publisher","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","volume":"34","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Yang, Q.: A survey on multi-task learning. IEEE Trans. Knowl. Data Eng. 34(12), 5586\u20135609 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72897-6_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T23:17:12Z","timestamp":1733095032000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72897-6_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031728969","9783031728976"],"references-count":59,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72897-6_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}