{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T23:24:51Z","timestamp":1768346691770,"version":"3.49.0"},"publisher-location":"Cham","reference-count":61,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031734137","type":"print"},{"value":"9783031734144","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"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\/978-3-031-73414-4_1","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T17:02:54Z","timestamp":1729789374000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["OvSW: Overcoming Silent Weights for\u00a0Accurate Binary Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5350-1528","authenticated-orcid":false,"given":"Jingyang","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1806-6676","authenticated-orcid":false,"given":"Zuohui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4632-9010","authenticated-orcid":false,"given":"Siqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4822-8939","authenticated-orcid":false,"given":"Yong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"1_CR1","unstructured":"Bengio, Y., L\u00e9onard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)"},{"key":"1_CR2","unstructured":"Brock, A., De, S., Smith, S.L., Simonyan, K.: High-performance large-scale image recognition without normalization. In: International Conference Machine Learning, pp. 1059\u20131071. PMLR (2021)"},{"key":"1_CR3","unstructured":"Bulat, A., Tzimiropoulos, G.: Xnor-net++: Improved binary neural networks. arXiv preprint arXiv:1909.13863 (2019)"},{"issue":"3","key":"1_CR4","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1109\/TNNLS.2020.2980041","volume":"32","author":"J Chen","year":"2020","unstructured":"Chen, J., Liu, L., Liu, Y., Zeng, X.: A learning framework for n-bit quantized neural networks toward fpgas. IEEE Trans. Neural Netw. Learn. Syst. 32(3), 1067\u20131081 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1_CR5","unstructured":"Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830 (2016)"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Ding, R., Chin, T.W., Liu, Z., Marculescu, D.: Regularizing activation distribution for training binarized deep networks. In: IEEE Conference Computer Vision Pattern Recognition, pp. 11408\u201311417 (2019)","DOI":"10.1109\/CVPR.2019.01167"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: Repvgg: making vgg-style convnets great again. In: IEEE Conference on Computer Vision Pattern Recognition, pp. 13733\u201313742 (2021)","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"on, Duan, Y., et al.: Transnas-bench-101: improving transferability and generalizability of cross-task neural architecture search. In: IEEE Conference on Computer Vision Pattern Recognition, pp. 5251\u20135260 (2021)","DOI":"10.1109\/CVPR46437.2021.00521"},{"key":"1_CR9","unstructured":"Feng, J.: Bolt. https:\/\/github.com\/huawei-noah\/bolt (2021)"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Gong, R., et al.: Differentiable soft quantization: bridging full-precision and low-bit neural networks. In: International Conference on Computer Vision, pp. 4852\u20134861 (2019)","DOI":"10.1109\/ICCV.2019.00495"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: International Conference Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: International Conference Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"issue":"9","key":"1_CR14","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/978-3-030-58580-8_14","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X He","year":"2020","unstructured":"He, X., et al.: ProxyBNN: learning binarized neural networks via proxy matrices. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 223\u2013241. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_14"},{"key":"1_CR17","unstructured":"Helwegen, K., Widdicombe, J., Geiger, L., Liu, Z., Cheng, K.T., Nusselder, R.: Latent weights do not exist: rethinking binarized neural network optimization. In: Advance Neural Information Processing System, vol.32 (2019)"},{"key":"1_CR18","unstructured":"Hinton, G., Vinyals, O., Dean, J., et\u00a0al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2(7) (2015)"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Horowitz, M.: 1.1 computing\u2019s energy problem (and what we can do about it). In: 2014 IEEE International Solid-state Circuits Conference Digest of Technical Papers (ISSCC), pp. 10\u201314. IEEE (2014)","DOI":"10.1109\/ISSCC.2014.6757323"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: IEEE Conference Computer Vision Pattern Recognition, pp. 2704\u20132713 (2018)","DOI":"10.1109\/CVPR.2018.00286"},{"key":"1_CR21","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"1_CR22","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"key":"1_CR23","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25 (2012)"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Lee, C., Kim, H., Park, E., Kim, J.J.: Insta-bnn: binary neural network with instance-aware threshold. In: International Conference Computer Vision, pp. 17325\u201317334 (2023)","DOI":"10.1109\/ICCV51070.2023.01589"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, D., Ham, B.: Network quantization with element-wise gradient scaling. In: IEEE Conference Computer Vision Pattern Recognition, pp. 6448\u20136457 (2021)","DOI":"10.1109\/CVPR46437.2021.00638"},{"key":"1_CR26","unstructured":"Li, H., Xu, Z., Taylor, G., Studer, C., Goldstein, T.: Visualizing the loss landscape of neural nets. Adv. Neural Inform. Process. Syst. (2018)"},{"key":"1_CR27","volume-title":"Learning efficient gans for image translation via differentiable masks and co-attention distillation","author":"S Li","year":"2022","unstructured":"Li, S., Lin, M., Wang, Y., Fei, C., Shao, L., Ji, R.: Learning efficient gans for image translation via differentiable masks and co-attention distillation. IEEE Trans, Multimedia (2022)"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"f Lin, M., et al.: Hrank: filter pruning using high-rank feature map. In: IEEE Conference Computer Vision Pattern Recognition, pp. 1529\u20131538 (2020)","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"1_CR29","volume-title":"Siman: Sign-to-magnitude network binarization","author":"M Lin","year":"2022","unstructured":"Lin, M., Ji, R., Xu, Z., Zhang, B., Chao, F., Lin, C.W., Shao, L.: Siman: Sign-to-magnitude network binarization. IEEE Trans. Pattern Anal. Mach, Intell (2022)"},{"key":"1_CR30","first-page":"7474","volume":"33","author":"M Lin","year":"2020","unstructured":"Lin, M., et al.: Rotated binary neural network. Adv. Neural Inform. Process. Syst. 33, 7474\u20137485 (2020)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"1_CR31","unstructured":"Lin, X., Zhao, C., Pan, W.: Towards accurate binary convolutional neural network. Adv. Neural Inform. Process. Syst. 30 (2017)"},{"key":"1_CR32","unstructured":"Liu, Z., Shen, Z., Li, S., Helwegen, K., Huang, D., Cheng, K.T.: How do adam and training strategies help bnns optimization. In: International Conference Machine Learning, pp. 6936\u20136946. PMLR (2021)"},{"key":"1_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-030-58568-6_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Shen, Z., Savvides, M., Cheng, K.-T.: ReActNet: towards precise binary neural network with generalized activation functions. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 143\u2013159. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_9"},{"key":"1_CR34","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wu, B., Luo, W., Yang, X., Liu, W., Cheng, K.T.: Bi-real net: enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm. In: European Conference Computer Vision, pp. 722\u2013737 (2018)","DOI":"10.1007\/978-3-030-01267-0_44"},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107461","volume":"107","author":"JH Luo","year":"2020","unstructured":"Luo, J.H., Wu, J.: Autopruner: an end-to-end trainable filter pruning method for efficient deep model inference. Pattern Recogn. 107, 107461 (2020)","journal-title":"Pattern Recogn."},{"key":"1_CR37","unstructured":"Martinez, B., Yang, J., Bulat, A., Tzimiropoulos, G.: Training binary neural networks with real-to-binary convolutions. arXiv preprint arXiv:2003.11535 (2020)"},{"key":"1_CR38","unstructured":"Nagel, M., Fournarakis, M., Bondarenko, Y., Blankevoort, T.: Overcoming oscillations in quantization-aware training. In: International Conference Machince Learning, pp. 16318\u201316330. PMLR (2022)"},{"key":"1_CR39","unstructured":"Paszke, A., et al.: PyTorch: an Imperative Style, High-Performance Deep Learning Library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9 Buc, F., Fox, E., Garnett, R. (eds.) Adv. Neural Inform. Process. Syst, pp. 8024\u20138035. Curran Associates, Inc. (2019). http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"1_CR40","doi-asserted-by":"crossref","unstructured":"Qin, H., et al.: Forward and backward information retention for accurate binary neural networks. In: IEEE Conference on Computer Vision Pattern Recognition, pp. 2250\u20132259 (2020)","DOI":"10.1109\/CVPR42600.2020.00232"},{"key":"1_CR41","doi-asserted-by":"crossref","unstructured":"Rajbhandari, S., Rasley, J., Ruwase, O., He, Y.: Zero: memory optimizations toward training trillion parameter models. In: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1\u201316. IEEE (2020)","DOI":"10.1109\/SC41405.2020.00024"},{"key":"1_CR42","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-319-46493-0_32","volume-title":"Computer Vision \u2013 ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV","author":"M Rastegari","year":"2016","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: imagenet classification using binary convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision \u2013 ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV, pp. 525\u2013542. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_32"},{"issue":"6","key":"1_CR43","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"1_CR44","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis."},{"key":"1_CR45","doi-asserted-by":"crossref","unstructured":"Shang, Y., Xu, D., Zong, Z., Yan, Y.: Network binarization via contrastive learning. arXiv preprint arXiv:2207.02970 (2022)","DOI":"10.1007\/978-3-031-20083-0_35"},{"key":"1_CR46","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1_CR47","doi-asserted-by":"crossref","unstructured":"Su, X., et al.: Prioritized architecture sampling with monto-carlo tree search. In: IEEE Conference on Computer Vision Pattern Recognition, pp. 10968\u201310977 (2021)","DOI":"10.1109\/CVPR46437.2021.01082"},{"key":"1_CR48","doi-asserted-by":"publisher","unstructured":"Tu, Z., Chen, X., Ren, P., Wang, Y.: Adabin: improving binary neural networks with adaptive binary sets. In: European Conference Computer Vision, pp. 379\u2013395. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20083-0_23","DOI":"10.1007\/978-3-031-20083-0_23"},{"key":"1_CR49","doi-asserted-by":"crossref","unstructured":"Wu, X.M., Zheng, D., Liu, Z., Zheng, W.S.: Estimator meets equilibrium perspective: a rectified straight through estimator for binary neural networks training. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 17055\u201317064 (2023)","DOI":"10.1109\/ICCV51070.2023.01564"},{"key":"1_CR50","doi-asserted-by":"crossref","unstructured":"Xu, S., et al.: Resilient binary neural network. AAAI. 37, 10620\u201310628 (2023)","DOI":"10.1609\/aaai.v37i9.26261"},{"key":"1_CR51","doi-asserted-by":"publisher","unstructured":"Xu, S., et al.: Recurrent bilinear optimization for binary neural networks. Eur. Conf. Comput. Vis. 19\u201335. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20053-3_2","DOI":"10.1007\/978-3-031-20053-3_2"},{"key":"1_CR52","first-page":"25553","volume":"34","author":"Y Xu","year":"2021","unstructured":"Xu, Y., Han, K., Xu, C., Tang, Y., Xu, C., Wang, Y.: Learning frequency domain approximation for binary neural networks. Adv. Neural Inform. Process. Syst. 34, 25553\u201325565 (2021)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"1_CR53","doi-asserted-by":"crossref","unstructured":"Xu, Z., et al.: Recu: reviving the dead weights in binary neural networks. In: International Conference Computer Vision, pp. 5198\u20135208 (2021)","DOI":"10.1109\/ICCV48922.2021.00515"},{"key":"1_CR54","doi-asserted-by":"crossref","unstructured":"Yang, J., et al.: Quantization networks. In: IEEE Conference Computing Vision Pattern Recognition, pp. 7308\u20137316 (2019)","DOI":"10.1109\/CVPR.2019.00748"},{"key":"1_CR55","first-page":"4091","volume":"33","author":"Z Yang","year":"2020","unstructured":"Yang, Z., et al.: Searching for low-bit weights in quantized neural networks. Adv. Neural Inform. Process. Syst. 33, 4091\u20134102 (2020)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"1_CR56","unstructured":"You, Y., Gitman, I., Ginsburg, B.: Large batch training of convolutional networks. arXiv preprint arXiv:1708.03888 (2017)"},{"key":"1_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yang, J., Ye, D., Hua, G.: Lq-nets: learned quantization for highly accurate and compact deep neural networks. In: European Conference on Computer Vision, pp. 365\u2013382 (2018)","DOI":"10.1007\/978-3-030-01237-3_23"},{"key":"1_CR58","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: IEEE Conference on Computer Vision Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1_CR59","doi-asserted-by":"crossref","unstructured":"Zhao, J., Yang, L., Zhang, B., Guo, G., Doermann, D.S.: Uncertainty-aware binary neural networks. In: IJCAI. pp. 3441\u20133447 (2021)","DOI":"10.24963\/ijcai.2021\/474"},{"key":"1_CR60","unstructured":"Zhou, S., Wu, Y., Ni, Z., Zhou, X., Wen, H., Zou, Y.: Dorefa-net: training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016)"},{"key":"1_CR61","doi-asserted-by":"crossref","unstructured":"Zhu, F., et al.: Towards unified int8 training for convolutional neural network. In: IEEE Conference on Computer Vision Pattern Recognition,. pp. 1969\u20131979 (2020)","DOI":"10.1109\/CVPR42600.2020.00204"}],"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\/978-3-031-73414-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T17:04:32Z","timestamp":1729789472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73414-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031734137","9783031734144"],"references-count":61,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73414-4_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 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"}}]}}