{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T22:11:22Z","timestamp":1775167882265,"version":"3.50.1"},"publisher-location":"Cham","reference-count":70,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031734038","type":"print"},{"value":"9783031734045","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"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\/978-3-031-73404-5_14","type":"book-chapter","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T16:03:13Z","timestamp":1730217793000},"page":"232-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Isomorphic Pruning for\u00a0Vision Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6935-0432","authenticated-orcid":false,"given":"Gongfan","family":"Fang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2819-8218","authenticated-orcid":false,"given":"Xinyin","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4930-1849","authenticated-orcid":false,"given":"Michael Bi","family":"Mi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0057-1404","authenticated-orcid":false,"given":"Xinchao","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Baum, E., Haussler, D.: What size net gives valid generalization? Advances in Neural Information Processing Systems, vol. 1 (1988)","DOI":"10.1162\/neco.1989.1.1.151"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Chavan, A., Shen, Z., Liu, Z., Liu, Z., Cheng, K.T., Xing, E.P.: Vision transformer slimming: multi-dimension searching in continuous optimization space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4931\u20134941 (2022)","DOI":"10.1109\/CVPR52688.2022.00488"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Chen, C.F.R., Fan, Q., Panda, R.: CrossViT: cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 357\u2013366 (2021)","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"14_CR4","unstructured":"Chen, T., Cheng, Y., Gan, Z., Yuan, L., Zhang, L., Wang, Z.: Chasing sparsity in vision transformers: an end-to-end exploration. In: Advances in Neural Information Processing Systems, vol. 34, pp. 19974\u201319988 (2021)"},{"key":"14_CR5","unstructured":"Chen, T., Liang, L., Ding, T., Zhu, Z., Zharkov, I.: OTOV2: automatic, generic, user-friendly. arXiv preprint arXiv:2303.06862 (2023)"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: RandAugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702\u2013703 (2020)","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"14_CR8","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: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"14_CR9","unstructured":"Ding, X., Ding, G., Guo, Y., Han, J., Yan, C.: Approximated oracle filter pruning for destructive CNN width optimization. In: International Conference on Machine Learning, pp. 1607\u20131616. PMLR (2019)"},{"key":"14_CR10","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Fang, G., Ma, X., Song, M., Mi, M.B., Wang, X.: DepGraph: towards any structural pruning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16091\u201316101 (2023)","DOI":"10.1109\/CVPR52729.2023.01544"},{"key":"14_CR12","unstructured":"Frantar, E., Alistarh, D.: SparseGPT: massive language models can be accurately pruned in one-shot (2023)"},{"key":"14_CR13","unstructured":"Goyal, P., et al.: Accurate, large minibatch SGD: training ImageNet in 1 hour. arXiv preprint arXiv:1706.02677 (2017)"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Guo, J., Ouyang, W., Xu, D.: Multi-dimensional pruning: a unified framework for model compression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1508\u20131517 (2020)","DOI":"10.1109\/CVPR42600.2020.00158"},{"key":"14_CR15","unstructured":"Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., Wang, Y.: Transformer in transformer. In: Advances in Neural Information Processing Systems, vol. 34, pp. 15908\u201315919 (2021)"},{"key":"14_CR16","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. In: International Conference on Learning Representations (ICLR) (2016)"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Hassibi, B., Stork, D.G., Wolff, G.J.: Optimal brain surgeon and general network pruning. In: IEEE International Conference on Neural Networks, pp. 293\u2013299. IEEE (1993)","DOI":"10.1109\/ICNN.1993.298572"},{"key":"14_CR18","unstructured":"He, H., et al.: Pruning self-attentions into convolutional layers in single path. arXiv preprint arXiv:2111.11802 (2021)"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y.: Soft filter pruning for accelerating deep convolutional neural networks. arXiv preprint arXiv:1808.06866 (2018)","DOI":"10.24963\/ijcai.2018\/309"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"He, Y., Liu, P., Wang, Z., Hu, Z., Yang, Y.: Filter pruning via geometric median for deep convolutional neural networks acceleration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4340\u20134349 (2019)","DOI":"10.1109\/CVPR.2019.00447"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1389\u20131397 (2017)","DOI":"10.1109\/ICCV.2017.155"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Hoffer, E., Ben-Nun, T., Hubara, I., Giladi, N., Hoefler, T., Soudry, D.: Augment your batch: improving generalization through instance repetition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8129\u20138138 (2020)","DOI":"10.1109\/CVPR42600.2020.00815"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"14_CR25","unstructured":"LeCun, Y., Denker, J., Solla, S.: Optimal brain damage. In: Advances in Neural Information Processing Systems, vol. 2 (1989)"},{"key":"14_CR26","unstructured":"Li, Y., et al.: EfficientFormer: vision transformers at MobileNet speed. In: Advances in Neural Information Processing Systems, vol. 35, pp. 12934\u201312949 (2022)"},{"issue":"12","key":"14_CR27","doi-asserted-by":"publisher","first-page":"7357","DOI":"10.1109\/TNNLS.2021.3084856","volume":"33","author":"M Lin","year":"2021","unstructured":"Lin, M., et al.: Network pruning using adaptive exemplar filters. IEEE Trans. Neural Netw. Learn. Syst. 33(12), 7357\u20137366 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Lin, M., Ji, R., Zhang, Y., Zhang, B., Wu, Y., Tian, Y.: Channel pruning via automatic structure search. arXiv preprint arXiv:2001.08565 (2020)","DOI":"10.24963\/ijcai.2020\/94"},{"key":"14_CR29","unstructured":"Liu, B., Wang, M., Foroosh, H., Tappen, M., Pensky, M.: Sparse convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 806\u2013814 (2015)"},{"key":"14_CR30","unstructured":"Liu, L., et al.: Group fisher pruning for practical network compression. In: International Conference on Machine Learning, pp. 7021\u20137032. PMLR (2021)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: MetaPruning: meta learning for automatic neural network channel pruning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3296\u20133305 (2019)","DOI":"10.1109\/ICCV.2019.00339"},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"14_CR34","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"14_CR35","unstructured":"Louizos, C., Welling, M., Kingma, D.P.: Learning sparse neural networks through $$ l_0 $$ regularization. arXiv preprint arXiv:1712.01312 (2017)"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Ma, X., Yuan, G., Lin, S., Li, Z., Sun, H., Wang, Y.: ResNet can be pruned 60$$\\times $$: introducing network purification and unused path removal (P-RM) after weight pruning. In: 2019 IEEE\/ACM International Symposium on Nanoscale Architectures (NANOARCH), pp.\u00a01\u20132. IEEE (2019)","DOI":"10.1109\/NANOARCH47378.2019.181304"},{"key":"14_CR37","unstructured":"Ma, X., Fang, G., Wang, X.: LLM-Pruner: on the structural pruning of large language models. In: Advances in Neural Information Processing Systems (2023)"},{"key":"14_CR38","unstructured":"Maintainers, Contributors: Torchvision: Pytorch\u2019s computer vision library. GitHub repository (2016)"},{"key":"14_CR39","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Mallya, A., Tyree, S., Frosio, I., Kautz, J.: Importance estimation for neural network pruning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11264\u201311272 (2019)","DOI":"10.1109\/CVPR.2019.01152"},{"key":"14_CR40","unstructured":"Molchanov, P., Tyree, S., Karras, T., Aila, T., Kautz, J.: Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440 (2016)"},{"key":"14_CR41","unstructured":"Peng, H., Wu, J., Chen, S., Huang, J.: Collaborative channel pruning for deep networks. In: International Conference on Machine Learning, pp. 5113\u20135122. PMLR (2019)"},{"key":"14_CR42","unstructured":"Pool, J., Sawarkar, A., Rodge, J.: Accelerating inference with sparsity using the NVIDIA ampere architecture and Nvidia TensorRT. NVIDIA Developer Technical Blog (2021). https:\/\/developer.nvidia.com\/blog\/accelerating-inference-with-sparsityusing-ampere-and-tensorrt"},{"key":"14_CR43","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Doll\u00e1r, P.: Designing network design spaces. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10428\u201310436 (2020)","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"14_CR44","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"14_CR45","unstructured":"Shi, D., Tao, C., Jin, Y., Yang, Z., Yuan, C., Wang, J.: UPop: unified and progressive pruning for compressing vision-language transformers. In: Proceedings of the 40th International Conference on Machine Learning, vol.\u00a0202, pp. 31292\u201331311. PMLR (2023)"},{"key":"14_CR46","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"14_CR47","unstructured":"Song, Z., Xu, Y., He, Z., Jiang, L., Jing, N., Liang, X.: CP-ViT: cascade vision transformer pruning via progressive sparsity prediction. arXiv preprint arXiv:2203.04570 (2022)"},{"key":"14_CR48","unstructured":"Sun, M., Liu, Z., Bair, A., Kolter, J.Z.: A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023)"},{"key":"14_CR49","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"14_CR50","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jegou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, vol.\u00a0139, pp. 10347\u201310357, July 2021"},{"key":"14_CR51","doi-asserted-by":"crossref","unstructured":"Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., J\u00e9gou, H.: Going deeper with image transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 32\u201342 (2021)","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"14_CR52","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"14_CR53","doi-asserted-by":"crossref","unstructured":"Voita, E., Talbot, D., Moiseev, F., Sennrich, R., Titov, I.: Analyzing multi-head self-attention: specialized heads do the heavy lifting, the rest can be pruned. arXiv preprint arXiv:1905.09418 (2019)","DOI":"10.18653\/v1\/P19-1580"},{"issue":"3","key":"14_CR54","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","volume":"8","author":"W Wang","year":"2022","unstructured":"Wang, W., et al.: PVT V2: improved baselines with pyramid vision transformer. Comput. Vis. Media 8(3), 415\u2013424 (2022)","journal-title":"Comput. Vis. Media"},{"key":"14_CR55","unstructured":"Wang, Z., Luo, H., Wang, P., Ding, F., Wang, F., Li, H.: VTC-LFC: vision transformer compression with low-frequency components. In: Advances in Neural Information Processing Systems, vol. 35, pp. 13974\u201313988 (2022)"},{"key":"14_CR56","unstructured":"Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"14_CR57","unstructured":"Wightman, R.: PyTorch-image-models. https:\/\/github.com\/huggingface\/pytorch-image-models"},{"key":"14_CR58","doi-asserted-by":"crossref","unstructured":"Yang, H., Yin, H., Shen, M., Molchanov, P., Li, H., Kautz, J.: Global vision transformer pruning with hessian-aware saliency. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18547\u201318557 (2023)","DOI":"10.1109\/CVPR52729.2023.01779"},{"key":"14_CR59","unstructured":"Ye, J., Lu, X., Lin, Z., Wang, J.Z.: Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers. arXiv preprint arXiv:1802.00124 (2018)"},{"key":"14_CR60","doi-asserted-by":"crossref","unstructured":"Yin, M., Uzkent, B., Shen, Y., Jin, H., Yuan, B.: GOHSP: a unified framework of graph and optimization-based heterogeneous structured pruning for vision transformer. arXiv preprint arXiv:2301.05345 (2023)","DOI":"10.1609\/aaai.v37i9.26298"},{"key":"14_CR61","doi-asserted-by":"crossref","unstructured":"Yu, F., Huang, K., Wang, M., Cheng, Y., Chu, W., Cui, L.: Width & depth pruning for vision transformers. In: AAAI Conference on Artificial Intelligence (AAAI), vol.\u00a02022 (2022)","DOI":"10.1609\/aaai.v36i3.20222"},{"issue":"7","key":"14_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-022-3646-6","volume":"66","author":"H Yu","year":"2023","unstructured":"Yu, H., Wu, J.: A unified pruning framework for vision transformers. Sci. China Inf. Sci. 66(7), 1\u20132 (2023)","journal-title":"Sci. China Inf. Sci."},{"key":"14_CR63","unstructured":"Yu, J., Huang, T.: AutoSlim: towards one-shot architecture search for channel numbers. arXiv preprint arXiv:1903.11728 (2019)"},{"key":"14_CR64","doi-asserted-by":"crossref","unstructured":"Yu, L., Xiang, W.: X-Pruner: eXplainable pruning for vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24355\u201324363 (2023)","DOI":"10.1109\/CVPR52729.2023.02333"},{"key":"14_CR65","unstructured":"Yu, S., et al.: Unified visual transformer compression. arXiv preprint arXiv:2203.08243 (2022)"},{"key":"14_CR66","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: CutMix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"14_CR67","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"14_CR68","unstructured":"Zheng, C., et al.: SAViT: structure-aware vision transformer pruning via collaborative optimization. In: Advances in Neural Information Processing Systems, vol. 35, pp. 9010\u20139023 (2022)"},{"key":"14_CR69","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 13001\u201313008 (2020)","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"14_CR70","unstructured":"Zhu, M., Tang, Y., Han, K.: Vision transformer pruning. arXiv preprint arXiv:2104.08500 (2021)"}],"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\/978-3-031-73404-5_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T19:45:43Z","timestamp":1745523943000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73404-5_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"ISBN":["9783031734038","9783031734045"],"references-count":70,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73404-5_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,30]]},"assertion":[{"value":"30 October 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"}}]}}