{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:02:53Z","timestamp":1784646173206,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":52,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819609710","type":"print"},{"value":"9789819609727","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"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\/978-981-96-0972-7_11","type":"book-chapter","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T08:10:58Z","timestamp":1733731858000},"page":"178-195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Foundation Model-Powered 3D Few-Shot Class Incremental Learning via\u00a0Training-Free Adaptor"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2161-7005","authenticated-orcid":false,"given":"Sahar","family":"Ahmadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3324-7849","authenticated-orcid":false,"given":"Ali","family":"Cheraghian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5168-2078","authenticated-orcid":false,"given":"Morteza","family":"Saberi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1198-5098","authenticated-orcid":false,"given":"Md.Towsif","family":"Abir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2818-4182","authenticated-orcid":false,"given":"Hamidreza","family":"Dastmalchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1513-8072","authenticated-orcid":false,"given":"Farookh","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7169-0318","authenticated-orcid":false,"given":"Shafin","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"11_CR1","unstructured":"Awais, M., Naseer, M., Khan, S., Anwer, R.M., Cholakkal, H., Shah, M., Yang, M.H., Khan, F.S.: Foundational models defining a new era in vision: A survey and outlook. arXiv preprint arXiv:2307.13721 (2023)"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Belouadah, E., Popescu, A.: Il2m: Class incremental learning with dual memory. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 583\u2013592 (2019)","DOI":"10.1109\/ICCV.2019.00067"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Belouadah, E., Popescu, A.: Scail: Classifier weights scaling for class incremental learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 1266\u20131275 (2020)","DOI":"10.1109\/WACV45572.2020.9093562"},{"key":"11_CR4","unstructured":"Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol.\u00a033, pp. 1877\u20131901. Curran Associates, Inc. (2020)"},{"key":"11_CR5","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Cao, X., Lu, H., Huang, L., Liu, X., Cheng, M.M.: Generative multi-modal models are good class incremental learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 28706\u201328717 (June 2024)","DOI":"10.1109\/CVPR52733.2024.02712"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Castro, F.M., Mar\u00edn-Jim\u00e9nez, M.J., Guil, N., Schmid, C., Alahari, K.: End-to-end incremental learning. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 233\u2013248 (2018)","DOI":"10.1007\/978-3-030-01258-8_15"},{"key":"11_CR8","unstructured":"Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et\u00a0al.: Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"key":"11_CR9","unstructured":"Chen, K., Lee, C.G.: Incremental few-shot learning via vector quantization in deep embedded space. In: International Conference on Learning Representations (2021)"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Cheraghian, A., Hayder, Z., Ramasinghe, S., Rahman, S., Jafaryahya, J., Petersson, L., Harandi, M.: Canonical shape projection is all you need for 3d few-shot class incremental learning. In: Computer Vision \u2013 ECCV 2024 (2024)","DOI":"10.1007\/978-3-031-72940-9_3"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Cheraghian, A., Rahman, S., Fang, P., Roy, S.K., Petersson, L., Harandi, M.: Semantic-aware knowledge distillation for few-shot class-incremental learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00256"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Cheraghian, A., Rahman, S., Ramasinghe, S., Fang, P., Simon, C., Petersson, L., Harandi, M.: Synthesized feature based few-shot class-incremental learning on a mixture of subspaces. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00854"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Chowdhury, T., Cheraghian, A., Ramasinghe, S., Ahmadi, S., Saberi, M., Rahman, S.: Few-shot class-incremental learning for 3d point cloud objects. In: European Conference on Computer Vision. pp. 204\u2013220. Springer (2022)","DOI":"10.1007\/978-3-031-20044-1_12"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Guo, M.H., Cai, J.X., Liu, Z.N., Mu, T.J., Martin, R.R., Hu, S.M.: Pct: Point cloud transformer. Computational Visual Media (2021)","DOI":"10.1007\/s41095-021-0229-5"},{"key":"11_CR15","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. In: International Conference on Learning Representations (2022), https:\/\/openreview.net\/forum?id=nZeVKeeFYf9"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Kim, J., Ku, Y., Kim, J., Cha, J., Baek, S.: Vlm-pl: Advanced pseudo labeling approach for class incremental object detection via vision-language model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. pp. 4170\u20134181 (June 2024)","DOI":"10.1109\/CVPRW63382.2024.00420"},{"key":"11_CR17","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: Pointcnn: Convolution on x-transformed points. In: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) (2018)"},{"issue":"12","key":"11_CR18","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935\u20132947 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00910"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Masci, J., Boscaini, D., Bronstein, M., Vandergheynst, P.: Geodesic convolutional neural networks on riemannian manifolds. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 37\u201345 (2015)","DOI":"10.1109\/ICCVW.2015.112"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Mazumder, P., Singh, P., Rai, P.: Few-shot lifelong learning. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i3.16334"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhao, K., Wang, T., Li, M., Lovell, B.C.: Few-shot class-incremental learning from an open-set perspective. In: European Conference on Computer Vision. pp. 382\u2013397. Springer (2022)","DOI":"10.1007\/978-3-031-19806-9_22"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Poulenard, A., Rakotosaona, M.J., Ponty, Y., Ovsjanikov, M.: Effective rotation-invariant point cnn with spherical harmonics kernels. In: Proceedings of the IEEE International Conference on 3D Vision (3DV) (2019)","DOI":"10.1109\/3DV.2019.00015"},{"key":"11_CR24","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., Guibas, L.J.: Volumetric and multi-view cnns for object classification on 3d data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5648\u20135656 (2016)","DOI":"10.1109\/CVPR.2016.609"},{"key":"11_CR26","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) (2017)"},{"key":"11_CR27","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. pp. 8748\u20138763. PMLR (2021)"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Rao, Y., Lu, J., Zhou, J.: Spherical fractal convolutional neural networks for point cloud recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00054"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Reizenstein, J., Shapovalov, R., Henzler, P., Sbordone, L., Labatut, P., Novotny, D.: Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 10901\u201310911 (2021)","DOI":"10.1109\/ICCV48922.2021.01072"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). pp. 945\u2013953 (2015)","DOI":"10.1109\/ICCV.2015.114"},{"key":"11_CR31","unstructured":"Sun, Q., Fang, Y., Wu, L., Wang, X., Cao, Y.: Eva-clip: Improved training techniques for clip at scale. arXiv preprint arXiv:2303.15389 (2023)"},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Tan, Y., Xiang, X.: Cross-domain few-shot incremental learning for point-cloud recognition. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 2307\u20132316 (January 2024)","DOI":"10.1109\/WACV57701.2024.00230"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Tan, Z., Ding, K., Guo, R., Liu, H.: Graph few-shot class-incremental learning. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (2022)","DOI":"10.1145\/3488560.3498455"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., Gong, Y.: Few-shot class-incremental learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01220"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Uy, M.A., Pham, Q.H., Hua, B.S., Nguyen, T., Yeung, S.K.: Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 1588\u20131597 (2019)","DOI":"10.1109\/ICCV.2019.00167"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Wang, C., Samari, B., Siddiqi, K.: Local spectral graph convolution for point set feature learning. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01225-0_4"},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (TOG) (2019)","DOI":"10.1145\/3326362"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: Pointconv: Deep convolutional networks on 3d point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00985"},{"key":"11_CR40","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1912\u20131920 (2015)"},{"key":"11_CR41","doi-asserted-by":"crossref","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Y.: Spidercnn: Deep learning on point sets with parameterized convolutional filters. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01237-3_6"},{"key":"11_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, B., Yuan, J., Shi, B., Chen, T., Li, Y., Qiao, Y.: Uni3d: A unified baseline for multi-dataset 3d object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9253\u20139262 (2023)","DOI":"10.1109\/CVPR52729.2023.00893"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, R., Guo, Z., Zhang, W., Li, K., Miao, X., Cui, B., Qiao, Y., Gao, P., Li, H.: Pointclip: Point cloud understanding by clip. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 8552\u20138562 (2022)","DOI":"10.1109\/CVPR52688.2022.00836"},{"key":"11_CR44","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 adaptation 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":"11_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Rabbat, M.: A graph-cnn for 3d point cloud classification. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2018)","DOI":"10.1109\/ICASSP.2018.8462291"},{"key":"11_CR46","unstructured":"Zhang, Y., Zhou, K., Liu, Z.: What makes good examples for visual in-context learning? In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems. vol.\u00a036, pp. 17773\u201317794. Curran Associates, Inc. (2023)"},{"key":"11_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"11_CR48","doi-asserted-by":"crossref","unstructured":"Zhou, D.W., Wang, F.Y., Ye, H.J., Ma, L., Pu, S., Zhan, D.C.: Forward compatible few-shot class-incremental learning. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00884"},{"key":"11_CR49","unstructured":"Zhou, J., Wang, J., Ma, B., Liu, Y.S., Huang, T., Wang, X.: Uni3d: Exploring unified 3d representation at scale. In: International Conference on Learning Representations (ICLR) (2024)"},{"key":"11_CR50","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.01631"},{"key":"11_CR51","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. International Journal of Computer Vision (IJCV) (2022)","DOI":"10.1007\/s11263-022-01653-1"},{"key":"11_CR52","doi-asserted-by":"crossref","unstructured":"Zhu, X., Zhang, R., He, B., Guo, Z., Zeng, Z., Qin, Z., Zhang, S., Gao, P.: Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 2639\u20132650 (2023)","DOI":"10.1109\/ICCV51070.2023.00249"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0972-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T09:05:00Z","timestamp":1733735100000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0972-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,10]]},"ISBN":["9789819609710","9789819609727"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0972-7_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,10]]},"assertion":[{"value":"10 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","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":"8 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}