{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T21:25:14Z","timestamp":1773350714881,"version":"3.50.1"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781919","type":"print"},{"value":"9783031781926","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"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\/978-3-031-78192-6_28","type":"book-chapter","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T11:20:46Z","timestamp":1733224846000},"page":"424-439","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improving Multi-label Recognition using Class Co-Occurrence Probabilities"],"prefix":"10.1007","author":[{"given":"Samyak","family":"Rawlekar","sequence":"first","affiliation":[]},{"given":"Shubhang","family":"Bhatnagar","sequence":"additional","affiliation":[]},{"given":"Vishnuvardhan Pogunulu","family":"Srinivasulu","sequence":"additional","affiliation":[]},{"given":"Narendra","family":"Ahuja","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Abdelfattah, R., Guo, Q., Li, X., Wang, X., Wang, S.: Cdul: Clip-driven unsupervised learning for multi-label image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 1348\u20131357 (2023)","DOI":"10.1109\/ICCV51070.2023.00130"},{"issue":"4","key":"28_CR2","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1109\/JBHI.2014.2308928","volume":"18","author":"MM Anthimopoulos","year":"2014","unstructured":"Anthimopoulos, M.M., Gianola, L., Scarnato, L., Diem, P., Mougiakakou, S.G.: A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE J. Biomed. Health Inform. 18(4), 1261\u20131271 (2014)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"28_CR3","unstructured":"Bhatnagar, S., Ahuja, N.: Piecewise-linear manifolds for deep metric learning. In: Conference on Parsimony and Learning. pp. 269\u2013281. PMLR (2024)"},{"key":"28_CR4","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. Advances in neural information processing systems 32 (2019)"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Chang, W.C., Jiang, D., Yu, H.F., Teo, C.H., Zhang, J., Zhong, K., Kolluri, K., Hu, Q., Shandilya, N., Ievgrafov, V., et\u00a0al.: Extreme multi-label learning for semantic matching in product search. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. pp. 2643\u20132651 (2021)","DOI":"10.1145\/3447548.3467092"},{"key":"28_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research 16, 321\u2013357 (2002)","journal-title":"Journal of artificial intelligence research"},{"issue":"3","key":"28_CR7","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/TPAMI.2020.3025814","volume":"44","author":"T Chen","year":"2020","unstructured":"Chen, T., Lin, L., Chen, R., Hui, X., Wu, H.: Knowledge-guided multi-label few-shot learning for general image recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1371\u20131384 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Chen, T., Xu, M., Hui, X., Wu, H., Lin, L.: Learning semantic-specific graph representation for multi-label image recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 522\u2013531 (2019)","DOI":"10.1109\/ICCV.2019.00061"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Z.M., Wei, X.S., Wang, P., Guo, Y.: Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 5177\u20135186 (2019)","DOI":"10.1109\/CVPR.2019.00532"},{"issue":"3","key":"28_CR10","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1109\/JBHI.2016.2636441","volume":"21","author":"G Ciocca","year":"2016","unstructured":"Ciocca, G., Napoletano, P., Schettini, R.: Food recognition: a new dataset, experiments, and results. IEEE J. Biomed. Health Inform. 21(3), 588\u2013598 (2016)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Cole, E., Mac\u00a0Aodha, O., Lorieul, T., Perona, P., Morris, D., Jojic, N.: Multi-label learning from single positive labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 933\u2013942 (2021)","DOI":"10.1109\/CVPR46437.2021.00099"},{"key":"28_CR12","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":"28_CR13","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"28_CR15","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":"28_CR16","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Ding, Z., Wang, A., Chen, H., Zhang, Q., Liu, P., Bao, Y., Yan, W., Han, J.: Exploring structured semantic prior for multi label recognition with incomplete labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3398\u20133407 (2023)","DOI":"10.1109\/CVPR52729.2023.00331"},{"key":"28_CR18","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"1","key":"28_CR19","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/j.0824-7935.2004.t01-1-00228.x","volume":"20","author":"A Estabrooks","year":"2004","unstructured":"Estabrooks, A., Jo, T., Japkowicz, N.: A multiple resampling method for learning from imbalanced data sets. Comput. Intell. 20(1), 18\u201336 (2004)","journal-title":"Comput. Intell."},{"key":"28_CR20","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88, 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vision"},{"issue":"2","key":"28_CR21","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s11263-023-01891-x","volume":"132","author":"P Gao","year":"2024","unstructured":"Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., Qiao, Y.: Clip-adapter: Better vision-language models with feature adapters. Int. J. Comput. Vision 132(2), 581\u2013595 (2024)","journal-title":"Int. J. Comput. Vision"},{"key":"28_CR22","unstructured":"Huang, H., Rawlekar, S., Chopra, S., Deniz, C.M.: Radiology reports improve visual representations learned from radiographs. In: Medical Imaging with Deep Learning. pp. 1385\u20131405. PMLR (2024)"},{"key":"28_CR23","doi-asserted-by":"publisher","unstructured":"Ilharco, G., Wortsman, M., Wightman, R., Gordon, C., Carlini, N., Taori, R., Dave, A., Shankar, V., Namkoong, H., Miller, J., Hajishirzi, H., Farhadi, A., Schmidt, L.: Openclip (Jul 2021). https:\/\/doi.org\/10.5281\/zenodo.5143773, https:\/\/doi.org\/10.5281\/zenodo.5143773, if you use this software, please cite it as below","DOI":"10.5281\/zenodo.5143773"},{"key":"28_CR24","unstructured":"Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International conference on machine learning. pp. 4904\u20134916. PMLR (2021)"},{"key":"28_CR25","unstructured":"Kang, B., Li, Y., Xie, S., Yuan, Z., Feng, J.: Exploring balanced feature spaces for representation learning. In: International Conference on Learning Representations (2020)"},{"key":"28_CR26","unstructured":"Karthik, S., Roth, K., Mancini, M., Akata, Z.: Vision-by-language for training-free compositional image retrieval. arXiv preprint arXiv:2310.09291 (2023)"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Khan, S., Hayat, M., Zamir, S.W., Shen, J., Shao, L.: Striking the right balance with uncertainty. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 103\u2013112 (2019)","DOI":"10.1109\/CVPR.2019.00019"},{"key":"28_CR28","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"28_CR29","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft COCO: Common Objects in Context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"28_CR30","doi-asserted-by":"crossref","unstructured":"Liu, F., Xiang, T., Hospedales, T.M., Yang, W., Sun, C.: Semantic regularisation for recurrent image annotation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2872\u20132880 (2017)","DOI":"10.1109\/CVPR.2017.443"},{"key":"28_CR31","unstructured":"Liu, W., Tsang, I.: On the optimality of classifier chain for multi-label classification. Advances in Neural Information Processing Systems 28 (2015)"},{"issue":"11","key":"28_CR32","doi-asserted-by":"publisher","first-page":"7955","DOI":"10.1109\/TPAMI.2021.3119334","volume":"44","author":"W Liu","year":"2021","unstructured":"Liu, W., Wang, H., Shen, X., Tsang, I.W.: The emerging trends of multi-label learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7955\u20137974 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR33","doi-asserted-by":"crossref","unstructured":"Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39(2), 539\u2013550 (2008)","DOI":"10.1109\/TSMCB.2008.2007853"},{"key":"28_CR34","doi-asserted-by":"crossref","unstructured":"Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 2537\u20132546 (2019)","DOI":"10.1109\/CVPR.2019.00264"},{"key":"28_CR35","unstructured":"Menon, A.K., Jayasumana, S., Rawat, A.S., Jain, H., Veit, A., Kumar, S.: Long-tail learning via logit adjustment. arXiv preprint arXiv:2007.07314 (2020)"},{"key":"28_CR36","doi-asserted-by":"crossref","unstructured":"Meyers, A., Johnston, N., Rathod, V., Korattikara, A., Gorban, A., Silberman, N., Guadarrama, S., Papandreou, G., Huang, J., Murphy, K.P.: Im2calories: towards an automated mobile vision food diary. In: Proceedings of the IEEE international conference on computer vision. pp. 1233\u20131241 (2015)","DOI":"10.1109\/ICCV.2015.146"},{"key":"28_CR37","doi-asserted-by":"crossref","unstructured":"Misra, I., Lawrence\u00a0Zitnick, C., Mitchell, M., Girshick, R.: Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2930\u20132939 (2016)","DOI":"10.1109\/CVPR.2016.320"},{"key":"28_CR38","doi-asserted-by":"crossref","unstructured":"Park, S., Lim, J., Jeon, Y., Choi, J.Y.: Influence-balanced loss for imbalanced visual classification. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 735\u2013744 (2021)","DOI":"10.1109\/ICCV48922.2021.00077"},{"key":"28_CR39","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748\u20138763. PMLR (2021)"},{"key":"28_CR40","doi-asserted-by":"crossref","unstructured":"Ridnik, T., Ben-Baruch, E., Zamir, N., Noy, A., Friedman, I., Protter, M., Zelnik-Manor, L.: Asymmetric loss for multi-label classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 82\u201391 (2021)","DOI":"10.1109\/ICCV48922.2021.00015"},{"key":"28_CR41","first-page":"30569","volume":"35","author":"X Sun","year":"2022","unstructured":"Sun, X., Hu, P., Saenko, K.: Dualcoop: Fast adaptation to multi-label recognition with limited annotations. Adv. Neural. Inf. Process. Syst. 35, 30569\u201330582 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"28_CR42","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: Cnn-rnn: A unified framework for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2285\u20132294 (2016)","DOI":"10.1109\/CVPR.2016.251"},{"key":"28_CR43","doi-asserted-by":"crossref","unstructured":"Wortsman, M., Ilharco, G., Kim, J.W., Li, M., Kornblith, S., Roelofs, R., Lopes, R.G., Hajishirzi, H., Farhadi, A., Namkoong, H., et\u00a0al.: Robust fine-tuning of zero-shot models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 7959\u20137971 (2022)","DOI":"10.1109\/CVPR52688.2022.00780"},{"key":"28_CR44","doi-asserted-by":"crossref","unstructured":"Wu, X., Fu, X., Liu, Y., Lim, E.P., Hoi, S.C., Sun, Q.: A large-scale benchmark for food image segmentation. In: Proceedings of the 29th ACM international conference on multimedia. pp. 506\u2013515 (2021)","DOI":"10.1145\/3474085.3475201"},{"key":"28_CR45","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhang, Z., Wei, F., Lin, Y., Cao, Y., Hu, H., Bai, X.: A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model. In: European Conference on Computer Vision. pp. 736\u2013753. Springer (2022)","DOI":"10.1007\/978-3-031-19818-2_42"},{"key":"28_CR46","doi-asserted-by":"crossref","unstructured":"Yang, J., Price, B., Cohen, S., Yang, M.H.: Context driven scene parsing with attention to rare classes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3294\u20133301 (2014)","DOI":"10.1109\/CVPR.2014.415"},{"key":"28_CR47","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.aiopen.2024.01.004","volume":"5","author":"Y Yao","year":"2024","unstructured":"Yao, Y., Zhang, A., Zhang, Z., Liu, Z., Chua, T.S., Sun, M.: Cpt: Colorful prompt tuning for pre-trained vision-language models. AI Open 5, 30\u201338 (2024)","journal-title":"AI Open"},{"key":"28_CR48","unstructured":"Yazici, V.O., Gonzalez-Garcia, A., Ramisa, A., Twardowski, B., Weijer, J.v.d.: Orderless recurrent models for multi-label classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 13440\u201313449 (2020)"},{"key":"28_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, F., Ahuja, N.: Open-nerf: Towards open vocabulary nerf decomposition. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 3456\u20133465 (2024)","DOI":"10.1109\/WACV57701.2024.00342"},{"key":"28_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, F., Qi, L., Yang, M.H., Ahuja, N.: Csl: Class-agnostic structure-constrained learning for segmentation including the unseen. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a038, pp. 7078\u20137086 (2024)","DOI":"10.1609\/aaai.v38i7.28535"},{"key":"28_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, R., Zhang, W., Fang, R., Gao, P., Li, K., Dai, J., Qiao, Y., Li, H.: Tip-adapter: Training-free adaption of clip for few-shot classification. In: European conference on computer vision. pp. 493\u2013510. Springer (2022)","DOI":"10.1007\/978-3-031-19833-5_29"},{"key":"28_CR52","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Pfister, T.: Learning fast sample re-weighting without reward data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 725\u2013734 (2021)","DOI":"10.1109\/ICCV48922.2021.00076"},{"issue":"9","key":"28_CR53","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. Int. J. Comput. Vision 130(9), 2337\u20132348 (2022)","journal-title":"Int. J. Comput. Vision"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78192-6_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T12:18:26Z","timestamp":1733228306000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78192-6_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"ISBN":["9783031781919","9783031781926"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78192-6_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"4 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}