{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T02:04:00Z","timestamp":1768356240249,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":55,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556786","type":"print"},{"value":"9789819556793","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-5679-3_24","type":"book-chapter","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T18:36:50Z","timestamp":1768329410000},"page":"343-357","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Divide, Weight, and\u00a0Route: Difficulty-Aware Optimization with\u00a0Dynamic Expert Fusion for\u00a0Long-Tailed Recognition"],"prefix":"10.1007","author":[{"given":"Xiaolei","family":"Wei","sequence":"first","affiliation":[]},{"given":"Yi","family":"Ouyang","sequence":"additional","affiliation":[]},{"given":"Haibo","family":"Ye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"24_CR1","unstructured":"Bai, J., et al.: On the effectiveness of out-of-distribution data in self-supervised long-tail learning. arXiv preprint arXiv:2306.04934 (2023)"},{"key":"24_CR2","first-page":"1","volume":"62","author":"Yu Bai","year":"2024","unstructured":"Bai, Yu., Shao, S., Zhao, S., Liu, W., Tao, D., Liu, B.: EME: energy-based multiexpert model for long-tailed remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 62, 1\u201312 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Cai, J., Wang, Y., Hwang, J.-N.: ACE: ally complementary experts for solving long-tailed recognition in one-shot. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 112\u2013121 (2021)","DOI":"10.1109\/ICCV48922.2021.00018"},{"key":"24_CR4","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Area: adaptive reweighting via effective area for long-tailed classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 19277\u201319287 (2023)","DOI":"10.1109\/ICCV51070.2023.01766"},{"key":"24_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-3-030-65414-6_9","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"H-P Chou","year":"2020","unstructured":"Chou, H.-P., Chang, S.-C., Pan, J.-Y., Wei, W., Juan, D.-C.: Remix: rebalanced mixup. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12540, pp. 95\u2013110. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-65414-6_9"},{"issue":"3","key":"24_CR7","first-page":"3695","volume":"45","author":"J Cui","year":"2022","unstructured":"Cui, J., Liu, S., Tian, Z., Zhong, Z., Jia, J.: Reslt: residual learning for long-tailed recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3695\u20133706 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Cui, J., Zhong, Z., Liu, S., Yu, B., Jia, J.: Parametric contrastive learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 715\u2013724 (2021)","DOI":"10.1109\/ICCV48922.2021.00075"},{"key":"24_CR9","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":"24_CR10","doi-asserted-by":"crossref","unstructured":"Du, F., Yang, P., Jia, Q., Nan, F., Chen, X., Yang, Y.: Global and local mixture consistency cumulative learning for long-tailed visual recognitions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15814\u201315823 (2023)","DOI":"10.1109\/CVPR52729.2023.01518"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Huang, W.: Exploring classification equilibrium in long-tailed object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3417\u20133426 (2021)","DOI":"10.1109\/ICCV48922.2021.00340"},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.cag.2022.07.020","volume":"107","author":"Y Feng","year":"2022","unstructured":"Feng, Y., et al.: Shrec\u201922 track: open-set 3D object retrieval. Comput. Graphics 107, 231\u2013240 (2022)","journal-title":"Comput. Graphics"},{"key":"24_CR13","doi-asserted-by":"publisher","first-page":"4764","DOI":"10.1109\/TMM.2022.3181789","volume":"25","author":"J Gao","year":"2022","unstructured":"Gao, J., Chen, J., Huazhu, F., Jiang, Y.-G.: Dynamic mixup for multi-label long-tailed food ingredient recognition. IEEE Trans. Multimedia 25, 4764\u20134773 (2022)","journal-title":"IEEE Trans. Multimedia"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"He, Y., Peng, L., Zhang, Y., Weng, J., Li, S., Luo, Z.: Long-tailed out-of-distribution detection: Prioritizing attention to tail. In; Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, 3446\u20133454 (2025)","DOI":"10.1609\/aaai.v39i3.32357"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Hong, Y., Han, S., Choi, K., Seo, S., Kim, B., Chang, B.: Disentangling label distribution for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6626\u20136636 (2021)","DOI":"10.1109\/CVPR46437.2021.00656"},{"key":"24_CR16","unstructured":"Hua, C., Xu, Q., Bao, S., Yang, Z., Huang, Q.: Reconboost: boosting can achieve modality reconcilement. In: International Conference on Machine Learning, pp. 19573\u201319597 (2024)"},{"key":"24_CR17","unstructured":"Hua, C., Xu, Q., Yang, Z., Wang, Z., Bao, S., Huang, Q.: OpenworldAUC: towards unified evaluation and optimization for open-world prompt tuning. In: Forty-second International Conference on Machine Learning (2025)"},{"key":"24_CR18","unstructured":"Jiang, Y., Hua, C., Feng, Y., Gao, Y.: Hierarchical set-to-set representation for 3-d cross-modal retrieval. IEEE TNNLS, 1\u201313 (2023)"},{"key":"24_CR19","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217 (2019)"},{"key":"24_CR20","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"key":"24_CR21","unstructured":"Li, F., Xu, Q., Bao, S., Han, B., Yang, Z., Huang, Q.: Hybrid generative fusion for efficient and privacy-preserving face recognition dataset generation. arXiv preprint arXiv:2508.10672 (2025)"},{"key":"24_CR22","unstructured":"Li, F., Xu, Q., Bao, S., Yang, Z., Cao, X., Huang, Q.: One image is worth a thousand words: a usability preservable text-image collaborative erasing framework. In: Forty-second International Conference on Machine Learning (2025)"},{"key":"24_CR23","unstructured":"Li, F., et al.: Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection. In: Proceedings of the 41st International Conference on Machine Learning, pp. 28989\u201329021 (2024)"},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Li, S., Gong, K., Liu, C.H., Wang, Y., Qiao, F., Cheng, X.: MetaSAug: meta semantic augmentation for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5212\u20135221 (2021)","DOI":"10.1109\/CVPR46437.2021.00517"},{"key":"24_CR25","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":"24_CR26","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":"24_CR27","doi-asserted-by":"crossref","unstructured":"Miao, W., Pang, G., Bai, X., Li, T., Zheng, J.: Out-of-distribution detection in long-tailed recognition with calibrated outlier class learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 4216\u20134224 (2024)","DOI":"10.1609\/aaai.v38i5.28217"},{"key":"24_CR28","first-page":"4175","volume":"33","author":"J Ren","year":"2020","unstructured":"Ren, J., Cunjun, Yu., Ma, X., Zhao, H., Yi, S., et al.: Balanced meta-softmax for long-tailed visual recognition. Adv. Neural. Inf. Process. Syst. 33, 4175\u20134186 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Shao, Z., et al.: MOL: joint estimation of micro-expression, optical flow, and landmark via transformer-graph-style convolution. IEEE Trans. Pattern Anal. Mach. Intell. 1\u201314 (2025)","DOI":"10.1109\/TPAMI.2025.3581162"},{"key":"24_CR30","doi-asserted-by":"crossref","unstructured":"Shao, Z., Li, F., Zhou, Y., Chen, H., Zhu, H., Yao, R.: Identity-invariant representation and transformer-style relation for micro-expression recognition. Appl. Intell. 19860\u201319871 (2023)","DOI":"10.1007\/s10489-023-04533-4"},{"key":"24_CR31","doi-asserted-by":"crossref","unstructured":"Shao, Z., Zhou, Y., Li, F., Zhu, H., Liu, B.: Joint facial action unit recognition and self-supervised optical flow estimation. Pattern Recogn. Lett. 70\u201376 (2024)","DOI":"10.1016\/j.patrec.2024.03.022"},{"key":"24_CR32","doi-asserted-by":"crossref","unstructured":"Sinha, S., Ohashi, H.: Difficulty-net: learning to predict difficulty for long-tailed recognition. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6444\u20136453 (2023)","DOI":"10.1109\/WACV56688.2023.00638"},{"key":"24_CR33","doi-asserted-by":"crossref","unstructured":"Sinha, S., Ohashi, H., Nakamura, K.: Class-wise difficulty-balanced loss for solving class-imbalance. In: Proceedings of the Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69544-6_33"},{"key":"24_CR34","doi-asserted-by":"crossref","unstructured":"Son, M., Koo, I., Park, J., Kim, C.: Difficulty-aware balancing margin loss for long-tailed recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 20522\u201320530 (2025)","DOI":"10.1609\/aaai.v39i19.34261"},{"key":"24_CR35","doi-asserted-by":"crossref","unstructured":"Tan, J., Lu, X., Zhang, G., Yin, C., Li, Q.: Equalization loss v2: a new gradient balance approach for long-tailed object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1685\u20131694 (2021)","DOI":"10.1109\/CVPR46437.2021.00173"},{"key":"24_CR36","doi-asserted-by":"crossref","unstructured":"Tan, J., et al.: Equalization loss for long-tailed object recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11662\u201311671 (2020)","DOI":"10.1109\/CVPR42600.2020.01168"},{"key":"24_CR37","unstructured":"Wang, H., et al.: Partial and asymmetric contrastive learning for out-of-distribution detection in long-tailed recognition. In: International Conference on Machine Learning, pp. 23446\u201323458. PMLR (2022)"},{"key":"24_CR38","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Seesaw loss for long-tailed instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9695\u20139704 (2021)","DOI":"10.1109\/CVPR46437.2021.00957"},{"key":"24_CR39","doi-asserted-by":"crossref","unstructured":"Wang, P., Han, K., Wei, X.-S., Zhang, L., Wang, L.: Contrastive learning based hybrid networks for long-tailed image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 943\u2013952 (2021)","DOI":"10.1109\/CVPR46437.2021.00100"},{"key":"24_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1007\/978-3-030-58568-6_43","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Wang","year":"2020","unstructured":"Wang, T., et al.: The devil is in classification: a simple framework for long-tail instance segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 728\u2013744. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_43"},{"key":"24_CR41","unstructured":"Wang, X., Lian, L., Miao, Z., Liu, Z., Yu, S.X.: Long-tailed recognition by routing diverse distribution-aware experts. arXiv preprint arXiv:2010.01809 (2020)"},{"key":"24_CR42","doi-asserted-by":"crossref","unstructured":"Wei, T., Wang, B.-L., Zhang, M.-L.: Eat: Towards long-tailed out-of-distribution detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 15787\u201315795 (2024)","DOI":"10.1609\/aaai.v38i14.29508"},{"key":"24_CR43","doi-asserted-by":"crossref","unstructured":"Wu, T., Liu, Z., Huang, Q., Wang, Y., Lin, D.: Adversarial robustness under long-tailed distribution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8659\u20138668 (2021)","DOI":"10.1109\/CVPR46437.2021.00855"},{"key":"24_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/978-3-030-58558-7_15","volume-title":"Computer Vision \u2013 ECCV 2020","author":"L Xiang","year":"2020","unstructured":"Xiang, L., Ding, G., Han, J.: Learning from multiple experts: self-paced knowledge distillation for long-tailed classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 247\u2013263. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_15"},{"key":"24_CR45","doi-asserted-by":"crossref","unstructured":"Yu, S., Guo, J., Zhang, R., Fan, Y., Wang, Z., Cheng, X.: A re-balancing strategy for class-imbalanced classification based on instance difficulty. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 70\u201379 (2022)","DOI":"10.1109\/CVPR52688.2022.00017"},{"key":"24_CR46","doi-asserted-by":"crossref","unstructured":"Zang, Y., Huang, C., Loy, C.C.: FASA: feature augmentation and sampling adaptation for long-tailed instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3457\u20133466 (2021)","DOI":"10.1109\/ICCV48922.2021.00344"},{"key":"24_CR47","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"24_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, Z., Yan, S., He, X., Sun, J.: Distribution alignment: a unified framework for long-tail visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2361\u20132370 (2021)","DOI":"10.1109\/CVPR46437.2021.00239"},{"key":"24_CR49","unstructured":"Zhang, Y., Hooi, B., Hong, L., Feng, J.: Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. In: Advances in Neural Information Processing Systems, pp. 34077\u201334090 (2022)"},{"key":"24_CR50","doi-asserted-by":"crossref","unstructured":"Zhao, Q., et al.: LTGC: long-tail recognition via leveraging LLMs-driven generated content. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19510\u201319520 (2024)","DOI":"10.1109\/CVPR52733.2024.01845"},{"key":"24_CR51","doi-asserted-by":"publisher","unstructured":"Zhao, Q., Dai, Y., Lin, S., Hu, W., Zhang, F., Liu, J.: LTRL: boosting long-tail recognition via reflective learning. In: European Conference on Computer Vision, pp. 1\u201318. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-72855-6_1","DOI":"10.1007\/978-3-031-72855-6_1"},{"key":"24_CR52","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Jiang, C., Hu, W., Zhang, F., Liu, J.: MDCS: more diverse experts with consistency self-distillation for long-tailed recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11597\u201311608 (2023)","DOI":"10.1109\/ICCV51070.2023.01065"},{"key":"24_CR53","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Cui, J., Liu, S., Jia, J.: Improving calibration for long-tailed recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16489\u201316498 (2021)","DOI":"10.1109\/CVPR46437.2021.01622"},{"key":"24_CR54","doi-asserted-by":"crossref","unstructured":"Zhou, B., Cui, Q., Wei, X.-S., Chen, Z.-M.: BBN: bilateral-branch network with cumulative learning for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9719\u20139728 (2020)","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"24_CR55","doi-asserted-by":"crossref","unstructured":"Zhu, J., Wang, Z., Chen, J., Chen, Y.-P.P., Jiang, Y.-G.: Balanced contrastive learning for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6908\u20136917 (2022)","DOI":"10.1109\/CVPR52688.2022.00678"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5679-3_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T18:36:59Z","timestamp":1768329419000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5679-3_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556786","9789819556793"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5679-3_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"14 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}