{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T23:48:36Z","timestamp":1782431316807,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"CCFLenovo Blue Ocean Research Fund"},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["No.CSTB2023NSCO-JOX0007"],"award-info":[{"award-number":["No.CSTB2023NSCO-JOX0007"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAAI-Huawei MindSpore Open Fund"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62176224, No.62222602"],"award-info":[{"award-number":["No.62176224, No.62222602"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["No.2023M731957"],"award-info":[{"award-number":["No.2023M731957"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No.2020AAA0108301"],"award-info":[{"award-number":["No.2020AAA0108301"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3612013","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T19:52:38Z","timestamp":1698436358000},"page":"490-498","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Cross-modal Unsupervised Domain Adaptation for 3D Semantic Segmentation via Bidirectional Fusion-then-Distillation"],"prefix":"10.1145","author":[{"given":"Yao","family":"Wu","sequence":"first","affiliation":[{"name":"Xiamen University, Xiamen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingwei","family":"Xing","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6153-5004","authenticated-orcid":false,"given":"Yachao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6945-7437","authenticated-orcid":false,"given":"Yuan","family":"Xie","sequence":"additional","affiliation":[{"name":"East China Normal University & Chongqing Institute of East China Normal University, Shanghai & Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2290-1785","authenticated-orcid":false,"given":"Jianping","family":"Fan","sequence":"additional","affiliation":[{"name":"Lenovo Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5216-3827","authenticated-orcid":false,"given":"Zhongchao","family":"Shi","sequence":"additional","affiliation":[{"name":"Lenovo Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8926-4162","authenticated-orcid":false,"given":"Yanyun","family":"Qu","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Iro Armeni Ozan Sener Amir R Zamir Helen Jiang Ioannis Brilakis Martin Fischer and Silvio Savarese. 2016. 3D semantic parsing of large-scale indoor spaces. In CVPR. 1534--1543.","DOI":"10.1109\/CVPR.2016.170"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Jens Behley Martin Garbade Andres Milioto Jan Quenzel Sven Behnke Cyrill Stachniss and Jurgen Gall. 2019. SemanticKITTI: A dataset for semantic scene understanding of lidar sequences. In ICCV. 9297--9307.","DOI":"10.1109\/ICCV.2019.00939"},{"key":"e_1_3_2_2_3_1","volume-title":"Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom.","author":"Caesar Holger","year":"2020","unstructured":"Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. 2020. nuScenes: A multimodal dataset for autonomous driving. In CVPR. 11621--11631."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Ran Cheng Ryan Razani Ehsan Taghavi Enxu Li and Bingbing Liu. 2021. (AF)2-S3Net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network. In CVPR. 12547--12556.","DOI":"10.1109\/CVPR46437.2021.01236"},{"key":"e_1_3_2_2_5_1","unstructured":"Christopher Choy JunYoung Gwak and Silvio Savarese. 2019. 4d spatio-temporal convnets: Minkowski convolutional neural networks. In CVPR. 3075--3084."},{"key":"e_1_3_2_2_6_1","volume-title":"Fast, uncertainty-aware semantic segmentation of lidar point clouds","author":"Cortinhal Tiago","unstructured":"Tiago Cortinhal, George Tzelepis, and Eren Erdal Aksoy. 2020. SalsaNext: Fast, uncertainty-aware semantic segmentation of lidar point clouds. In ISVC. Springer, 207--222."},{"key":"e_1_3_2_2_7_1","unstructured":"Angela Dai Angel X Chang Manolis Savva Maciej Halber Thomas Funkhouser and Matthias Nie\u00dfner. 2017. ScanNet: Richly-annotated 3d reconstructions of indoor scenes. In CVPR. 5828--5839."},{"key":"e_1_3_2_2_8_1","volume-title":"Omar Nasr, Hanan Kamel, and Senthil Yogamani.","author":"Madawi Khaled El","year":"2019","unstructured":"Khaled El Madawi, Hazem Rashed, Ahmad El Sallab, Omar Nasr, Hanan Kamel, and Senthil Yogamani. 2019. Rgb and lidar fusion based 3d semantic segmentation for autonomous driving. In ITSC. IEEE, 7--12."},{"key":"e_1_3_2_2_9_1","volume-title":"Learning 3D semantic segmentation with only 2D image supervision. In 3DV","author":"Genova Kyle","unstructured":"Kyle Genova, Xiaoqi Yin, Abhijit Kundu, Caroline Pantofaru, Forrester Cole, Avneesh Sud, Brian Brewington, Brian Shucker, and Thomas Funkhouser. 2021. Learning 3D semantic segmentation with only 2D image supervision. In 3DV. IEEE, 361--372."},{"key":"e_1_3_2_2_10_1","volume-title":"Maximilian M\u00fchlegg, Sebastian Dorn, et al.","author":"Geyer Jakob","year":"2020","unstructured":"Jakob Geyer, Yohannes Kassahun, Mentar Mahmudi, Xavier Ricou, Rupesh Durgesh, Andrew S Chung, Lorenz Hauswald, Viet Hoang Pham, Maximilian M\u00fchlegg, Sebastian Dorn, et al. 2020. A2D2: Audi autonomous driving dataset. arXiv preprint arXiv:2004.06320 (2020)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Benjamin Graham Martin Engelcke and Laurens Van Der Maaten. 2018. 3d semantic segmentation with submanifold sparse convolutional networks. In CVPR. 9224--9232.","DOI":"10.1109\/CVPR.2018.00961"},{"key":"e_1_3_2_2_12_1","first-page":"5436","article-title":"Beyond self-attention: External attention using two linear layers for visual tasks","volume":"45","author":"Guo Meng-Hao","year":"2022","unstructured":"Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu, and Shi-Min Hu. 2022. Beyond self-attention: External attention using two linear layers for visual tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, 5 (2022), 5436--5447.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_14_1","volume-title":"ICML. Pmlr","author":"Hoffman Judy","year":"2018","unstructured":"Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell. 2018. CyCADA: Cycle-consistent adversarial domain adaptation. In ICML. Pmlr, 1989--1998."},{"key":"e_1_3_2_2_15_1","volume-title":"Chen Change Loy, and Yikang Li","author":"Hou Yuenan","year":"2022","unstructured":"Yuenan Hou, Xinge Zhu, Yuexin Ma, Chen Change Loy, and Yikang Li. 2022. Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation. In CVPR. 8479--8488."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Lukas Hoyer Dengxin Dai and Luc Van Gool. 2022a. DAFormer: Improving network architectures and training strategies for domain-adaptive semantic segmentation. In CVPR. 9924--9935.","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"e_1_3_2_2_17_1","volume-title":"HRDA: Context-aware high-resolution domain-adaptive semantic segmentation","author":"Hoyer Lukas","year":"2022","unstructured":"Lukas Hoyer, Dengxin Dai, and Luc Van Gool. 2022b. HRDA: Context-aware high-resolution domain-adaptive semantic segmentation. In ECCV. Springer, 372--391."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Jie Hu Li Shen and Gang Sun. 2018. Squeeze-and-excitation networks. In CVPR. 7132--7141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_2_2_19_1","unstructured":"Qingyong Hu Bo Yang Linhai Xie Stefano Rosa Yulan Guo Zhihua Wang Niki Trigoni and Andrew Markham. 2020. RandLA-Net: Efficient semantic segmentation of large-scale point clouds. In CVPR. 11108--11117."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Maximilian Jaritz Tuan-Hung Vu Raoul de Charette Emilie Wirbel and Patrick P\u00e9rez. 2020. xMUDA: Cross-modal unsupervised domain adaptation for 3d semantic segmentation. In CVPR. 12605--12614.","DOI":"10.1109\/CVPR42600.2020.01262"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Georg Krispel Michael Opitz Georg Waltner Horst Possegger and Horst Bischof. 2020. FuseSeg: Lidar point cloud segmentation fusing multi-modal data. In WACV. 1874--1883.","DOI":"10.1109\/WACV45572.2020.9093584"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Miaoyu Li Yachao Zhang Yuan Xie Zuodong Gao Cuihua Li Zhizhong Zhang and Yanyun Qu. 2022. Cross-Domain and Cross-Modal Knowledge Distillation in Domain Adaptation for 3D Semantic Segmentation. In ACMMM. 3829--3837.","DOI":"10.1145\/3503161.3547990"},{"key":"e_1_3_2_2_23_1","unstructured":"Yunsheng Li Lu Yuan and Nuno Vasconcelos. 2019. Bidirectional learning for domain adaptation of semantic segmentation. In CVPR. 6936--6945."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.04.012"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Yawei Luo Liang Zheng Tao Guan Junqing Yu and Yi Yang. 2019. Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation. In CVPR. 2507--2516.","DOI":"10.1109\/CVPR.2019.00261"},{"key":"e_1_3_2_2_26_1","volume-title":"RangeNet: Fast and accurate lidar semantic segmentation","author":"Milioto Andres","unstructured":"Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyrill Stachniss. 2019. RangeNet: Fast and accurate lidar semantic segmentation. In IROS. IEEE, 4213--4220."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Duo Peng Yinjie Lei Wen Li Pingping Zhang and Yulan Guo. 2021. Sparse-to-dense feature matching: Intra and inter domain cross-modal learning in domain adaptation for 3d semantic segmentation. In ICCV. 7108--7117.","DOI":"10.1109\/ICCV48922.2021.00702"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Charles R Qi Hao Su Kaichun Mo and Leonidas J Guibas. 2017a. PointNet: Deep learning on point sets for 3d classification and segmentation. In CVPR. 652--660.","DOI":"10.1109\/CVPR.2017.16"},{"key":"e_1_3_2_2_29_1","volume-title":"PointNet: Deep hierarchical feature learning on point sets in a metric space. NeurIPS","author":"Qi Charles Ruizhongtai","year":"2017","unstructured":"Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. 2017b. PointNet: Deep hierarchical feature learning on point sets in a metric space. NeurIPS (2017), 5099--5108."},{"key":"e_1_3_2_2_30_1","volume-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger Olaf","unstructured":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In MICCAI. Springer, 234--241."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_2_32_1","volume-title":"CosMix: Compositional semantic mix for domain adaptation in 3d lidar segmentation","author":"Saltori Cristiano","unstructured":"Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni, Nicu Sebe, Elisa Ricci, and Fabio Poiesi. 2022. CosMix: Compositional semantic mix for domain adaptation in 3d lidar segmentation. In ECCV. Springer, 586--602."},{"key":"e_1_3_2_2_33_1","volume-title":"Searching efficient 3d architectures with sparse point-voxel convolution","author":"Tang Haotian","unstructured":"Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. 2020a. Searching efficient 3d architectures with sparse point-voxel convolution. In ECCV. Springer, 685--702."},{"key":"e_1_3_2_2_34_1","volume-title":"Searching efficient 3d architectures with sparse point-voxel convolution","author":"Tang Haotian","unstructured":"Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. 2020b. Searching efficient 3d architectures with sparse point-voxel convolution. In ECCV. Springer, 685--702."},{"key":"e_1_3_2_2_35_1","volume-title":"Francc ois Goulette, and Leonidas J Guibas","author":"Thomas Hugues","year":"2019","unstructured":"Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Francc ois Goulette, and Leonidas J Guibas. 2019. KPConv: Flexible and deformable convolution for point clouds. In ICCV. 6411--6420."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Yi-Hsuan Tsai Wei-Chih Hung Samuel Schulter Kihyuk Sohn Ming-Hsuan Yang and Manmohan Chandraker. 2018. Learning to adapt structured output space for semantic segmentation. In CVPR. 7472--7481.","DOI":"10.1109\/CVPR.2018.00780"},{"key":"e_1_3_2_2_37_1","unstructured":"Tuan-Hung Vu Himalaya Jain Maxime Bucher Matthieu Cord and Patrick P\u00e9rez. 2019. AdvEnt: Adversarial entropy minimization for domain adaptation in semantic segmentation. In CVPR. 2517--2526."},{"key":"e_1_3_2_2_38_1","volume-title":"SqueezeSeg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud","author":"Wu Bichen","year":"1887","unstructured":"Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer. 2018. SqueezeSeg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud. In ICRA. IEEE, 1887--1893."},{"key":"e_1_3_2_2_39_1","volume-title":"SqueezeSegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud","author":"Wu Bichen","unstructured":"Bichen Wu, Xuanyu Zhou, Sicheng Zhao, Xiangyu Yue, and Kurt Keutzer. 2019b. SqueezeSegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud. In ICRA. IEEE, 4376--4382."},{"key":"e_1_3_2_2_40_1","unstructured":"Wenxuan Wu Zhongang Qi and Li Fuxin. 2019a. PointConv: Deep convolutional networks on 3d point clouds. In CVPR. 9621--9630."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i3.20183"},{"key":"e_1_3_2_2_42_1","volume-title":"SqueezeSegv3: Spatially-adaptive convolution for efficient point-cloud segmentation","author":"Xu Chenfeng","unstructured":"Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka. 2020. SqueezeSegv3: Spatially-adaptive convolution for efficient point-cloud segmentation. In ECCV. Springer, 1--19."},{"key":"e_1_3_2_2_43_1","unstructured":"Jianyun Xu Ruixiang Zhang Jian Dou Yushi Zhu Jie Sun and Shiliang Pu. 2021. RPVNet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In ICCV. 16024--16033."},{"key":"e_1_3_2_2_44_1","volume-title":"2DPASS: 2d priors assisted semantic segmentation on lidar point clouds","author":"Yan Xu","unstructured":"Xu Yan, Jiantao Gao, Chaoda Zheng, Chao Zheng, Ruimao Zhang, Shuguang Cui, and Zhen Li. 2022. 2DPASS: 2d priors assisted semantic segmentation on lidar point clouds. In ECCV. Springer, 677--695."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","unstructured":"Li Yi Boqing Gong and Thomas Funkhouser. 2021. Complete & Label: A domain adaptation approach to semantic segmentation of lidar point clouds. In CVPR. 15363--15373.","DOI":"10.1109\/CVPR46437.2021.01511"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"crossref","unstructured":"Pan Zhang Bo Zhang Ting Zhang Dong Chen Yong Wang and Fang Wen. 2021. Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation. In CVPR. 12414--12424.","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Yachao Zhang Miaoyu Li Yuan Xie Cuihua Li Cong Wang Zhizhong Zhang and Yanyun Qu. 2022. Self-supervised Exclusive Learning for 3D Segmentation with Cross-Modal Unsupervised Domain Adaptation. In ACMMM. 3338--3346.","DOI":"10.1145\/3503161.3547987"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"crossref","unstructured":"Yang Zhang Zixiang Zhou Philip David Xiangyu Yue Zerong Xi Boqing Gong and Hassan Foroosh. 2020. PolarNet: An improved grid representation for online lidar point clouds semantic segmentation. In CVPR. 9601--9610.","DOI":"10.1109\/CVPR42600.2020.00962"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i4.16464"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01395-y"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Zhuangwei Zhuang Rong Li Kui Jia Qicheng Wang Yuanqing Li and Mingkui Tan. 2021. Perception-aware multi-sensor fusion for 3d lidar semantic segmentation. In ICCV. 16280--16290.","DOI":"10.1109\/ICCV48922.2021.01597"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","location":"Ottawa ON Canada","acronym":"MM '23","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612013","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3612013","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:36Z","timestamp":1755820836000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612013"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":51,"alternative-id":["10.1145\/3581783.3612013","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3612013","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}