{"type":"ScholarlyArticle","title":"Contrastive and View-Interaction Structure Learning for Multi-view Clustering","authors":[{"name":"Jing Wang","slug":"jing-wang","given":"Jing","family":"Wang","affiliation":"Key Laboratory of Big Data & Artificial Intelligence in Transportation ( Beijing Jiaotong University) , Ministry of Education; School of Computer Science and Technology , Beijing Jiaotong University , Beijing 100044 , China"},{"name":"Songhe Feng","slug":"songhe-feng","given":"Songhe","family":"Feng","affiliation":"Key Laboratory of Big Data & Artificial Intelligence in Transportation ( Beijing Jiaotong University) , Ministry of Education; School of Computer Science and Technology , Beijing Jiaotong University , Beijing 100044 , China"}],"abstract":"Existing Deep Multi-view Clustering (DMVC) approaches typically concentrate on capturing consensus semantics from multiple views, where contrastive learning is widely used to align view-specific representations of each view. Unfortunately, view-specific representations are extracted from the content information of the corresponding instance, neglecting the relationships among different instances. Furthermore, existing contrastive loss imports numerous false negative pairs that conflict with the clustering objectives. In response to these challenges, we propose a contraStive and viEw-interaction stRucture learning framework for multI-viEw cluStering (SERIES). Our method takes into account the structural relations among instances and boosts the contrastive loss to improve intra-class compactness.  Meanwhile, a cross-view dual relation generation mechanism is introduced to achieve the consensus structural graph across multiple views for clustering.  Specifically, we initially acquire view-specific representations using multiple graph autoencoders to exploit both content information and structural information. Furthermore, to pull together the same cluster instances, a soft negative pair aware contrastive loss is employed to distinguish the dissimilar instances while attracting similar instances. Thereafter, the view-specific representations are fed into cross-view dual relation generation layers to generate the affinity matrices of each other, aiming to reveal a consistent structural graph across various views. Extensive experiments conducted on six benchmarks illustrate the superiority of our method compared to other state-of-the-art approaches.","doi":"10.24963/ijcai.2024/559","url":"https://ijcai.org/proceedings/2024/559","pdf":"https://ijcai.org/proceedings/2024/0559.pdf","markdown":"https://ijcai.org/proceedings/2024/559.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/559","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":559,"pages":{"first":5055,"last":5063},"keywords":["Machine Learning: ML: Clustering","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Amini et al., 2009] Massih-Reza Amini, Nicolas Usunier, and Cyril Goutte. Learning from multiple partially ob- served views -an application to multilingual text catego- rization. In Advances in Neural Information Processing Systems, pages 28-36, 2009.","title":"Learning from multiple partially observed views -an application to multilingual text categorization","authors":"Massih-Reza Amini, Nicolas Usunier, Cyril Goutte","year":"2009","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cao et al., 2015] Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, and Hua Zhang. Diversity-induced multi-view subspace clustering. In IEEE Conference on Computer Vi- sion and Pattern Recognition, pages 586-594, 2015.","title":"Diversity-induced multi-view subspace clustering","authors":"Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, Hua Zhang","year":"2015","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2020] Ting Chen, Simon Kornblith, Moham- mad Norouzi, and Geoffrey E. Hinton. A simple frame- work for contrastive learning of visual representations. In International Conference on Machine Learning, pages 1597-1607, 2020.","title":"A simple framework for contrastive learning of visual representations","authors":"Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E Hinton","year":"2020","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2022] Man-Sheng Chen, Jia-Qi Lin, Xiang- Long Li, Bao-Yu Liu, Chang-Dong Wang, Dong Huang, and Jian-Huang Lai. Representation learning in multi- view clustering: a literature review. Data Science and En- gineering, pages 225-241, 2022.","title":"Representation learning in multiview clustering: a literature review","authors":"Chen","year":"2022","venue":"Data Science and Engineering","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Fan et al., 2020] Shaohua Fan, Xiao Wang, Chuan Shi, Emiao Lu, Ken Lin, and Bai Wang. One2multi graph au- toencoder for multi-view graph clustering. In Proceedings of The Web Conference, pages 3070-3076, 2020.","title":"One2multi graph autoencoder for multi-view graph clustering","authors":"Xiao Shaohua Fan, Chuan Wang, Emiao Shi, Ken Lu, Bai Lin, Wang","year":"2020","venue":"Proceedings of The Web Conference","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Huang et al., 2022] Shudong Huang, Yixi Liu, Ivor W Tsang, Zenglin Xu, and Jiancheng Lv. Multi-view sub- space clustering by joint measuring of consistency and di- versity. IEEE Transactions on Knowledge and Data Engi- neering, pages 8270-8281, 2022.","title":"Multi-view subspace clustering by joint measuring of consistency and diversity","authors":"Huang","year":"2022","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Huang et al., 2023] Zongmo Huang, Yazhou Ren, Xiaorong Pu, Shudong Huang, Zenglin Xu, and Lifang He. Self- supervised graph attention networks for deep weighted multi-view clustering. In Conference on Artificial Intel- ligence, pages 7936-7943, 2023.","title":"Selfsupervised graph attention networks for deep weighted multi-view clustering","authors":"Huang","year":"2023","venue":"Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hwang and Grauman, 2010] Sung Ju Hwang and Kristen Grauman. Accounting for the relative importance of ob- jects in image retrieval. In BMVC, page 5, 2010.","title":"Accounting for the relative importance of objects in image retrieval","authors":"Ju Sung, Kristen Hwang, Grauman","year":"2010","venue":"BMVC","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kang et al., 2020] Zhao Kang, Wangtao Zhou, Zhitong Zhao, Junming Shao, Meng Han, and Zenglin Xu. Large- scale multi-view subspace clustering in linear time. In Conference on Artificial Intelligence, pages 4412-4419, 2020.","title":"Largescale multi-view subspace clustering in linear time","authors":"Zhao Kang, Wangtao Zhou, Zhitong Zhao, Junming Shao, Meng Han, Zenglin Xu","year":"2020","venue":"Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kipf and Welling, 2017] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Rep- resentations, pages 1-14, 2017.","title":"Semi-supervised classification with graph convolutional networks","authors":"Thomas N Kipf, Max Welling","year":"2017","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kumar et al., 2011] Abhishek Kumar, Piyush Rai, and Hal Daume. Co-regularized multi-view spectral clustering. Advances in Neural Information Processing Systems, pages 1-9, 2011.","title":"Co-regularized multi-view spectral clustering","authors":"Abhishek Kumar, Piyush Rai, Hal Daume","year":"2011","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2023] Haobin Li, Yunfan Li, Mouxing Yang, Peng Hu, Dezhong Peng, and Xi Peng. Incomplete multi-view clustering via prototype-based imputation. arXiv preprint, pages 1-9, 2023.","title":"Incomplete multi-view clustering via prototype-based imputation","authors":"Li","year":"2023","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2023/435"},{"position":13,"raw":"[Lin et al., 2021] Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, and Xi Peng. Completer: Incomplete multi-view clustering via contrastive prediction. In IEEE Conference on Computer Vision and Pattern Recognition, pages 11174-11183, 2021.","title":"Completer: Incomplete multi-view clustering via contrastive prediction","authors":"Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, Xi Peng","year":"2021","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2020] Xinwang Liu, Miaomiao Li, Chang Tang, Jingyuan Xia, Jian Xiong, Li Liu, Marius Kloft, and En Zhu. Efficient and effective regularized incomplete multi-view clustering. IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, pages 2634-2646, 2020.","title":"Efficient and effective regularized incomplete multi-view clustering","authors":"Xinwang Liu, Miaomiao Li, Chang Tang, Jingyuan Xia, Jian Xiong, Li Liu, Marius Kloft, En Zhu","year":"2020","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2023] Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Qing Liao, and Yuanqing Xia. Contrastive multi-view ker- nel learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 9552-9566, 2023.","title":"Contrastive multi-view kernel learning","authors":"Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Qing Liao, Yuanqing Xia","year":"2023","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Perkins and Theiler, 2003] Simon Perkins and James Theiler. Online feature selection using grafting. In International Conference on Machine Learning, pages 592-599, 2003.","title":"Online feature selection using grafting","authors":"Theiler Perkins, Simon Perkins, James Theiler","year":"2003","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Tan et al., 2023] Yuze Tan, Yixi Liu, Hongjie Wu, Jiancheng Lv, and Shudong Huang. Metric multi-view graph clustering. In Conference on Artificial Intelligence, pages 9962-9970, 2023.","title":"Metric multi-view graph clustering","authors":"Tan","year":"2023","venue":"Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Tang et al., 2022] Kewei Tang, Kaiqiang Xu, Wei Jiang, Zhixun Su, Xiyan Sun, and XiaoNan Luo. Selecting the best part from multiple laplacian autoencoders for multi- view subspace clustering. IEEE Transactions on Knowl- edge and Data Engineering, pages 7457-7469, 2022.","title":"Selecting the best part from multiple laplacian autoencoders for multiview subspace clustering","authors":"Kewei Tang, Kaiqiang Xu, Wei Jiang, Zhixun Su, Xiyan Sun, Xiaonan Luo","year":"2022","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Trosten et al., 2021] Daniel J Trosten, Sigurd Lokse, Robert Jenssen, and Michael Kampffmeyer. Reconsidering rep- resentation alignment for multi-view clustering. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1255-1265, 2021.","title":"Reconsidering representation alignment for multi-view clustering","authors":"Sigurd Daniel J Trosten, Robert Lokse, Michael Jenssen, Kampffmeyer","year":"2021","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Trosten et al., 2023] Daniel J Trosten, Sigurd Løkse, Robert Jenssen, and Michael C Kampffmeyer. On the effects of self-supervision and contrastive alignment in deep multi- view clustering. In IEEE Conference on Computer Vision and Pattern Recognition, pages 23976-23985, 2023.","title":"On the effects of self-supervision and contrastive alignment in deep multiview clustering","authors":"Sigurd Daniel J Trosten, Robert Løkse, Michael C Jenssen, Kampffmeyer","year":"2023","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Van der Maaten and Hinton, 2008] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning research, pages 2579-2605, 2008.","title":"Visualizing data using t-sne","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":"Journal of Machine Learning research","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Velickovic et al., 2018] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, and Adriana Romero. Graph attention networks. In International Conference on Learning Rep- resentations, pages 1-12, 2018.","title":"Graph attention networks","authors":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2015] Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. On deep multi-view represen- tation learning. In International Conference on Machine Learning, pages 1083-1092, 2015.","title":"On deep multi-view representation learning","authors":"Wang","year":"2015","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2019] Hao Wang, Yan Yang, and Bing Liu. Gmc: Graph-based multi-view clustering. IEEE Transac- tions on Knowledge and Data Engineering, pages 1116- 1129, 2019.","title":"Gmc: Graph-based multi-view clustering","authors":"Hao Wang, Yan Yang, Bing Liu","year":"2019","venue":"IEEE Transac-tions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2021] Yiming Wang, Dongxia Chang, Zhiqiang Fu, and Yao Zhao. Consistent multiple graph embedding for multi-view clustering. IEEE Transactions on Multimedia, pages 1008-1018, 2021.","title":"Consistent multiple graph embedding for multi-view clustering","authors":"Yiming Wang, Dongxia Chang, Zhiqiang Fu, Yao Zhao","year":"2021","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wen et al., 2020a] Jie Wen, Ke Yan, Zheng Zhang, Yong Xu, Junqian Wang, Lunke Fei, and Bob Zhang. Adap- tive graph completion based incomplete multi-view clus- tering. IEEE Transactions on Multimedia, pages 2493- 2504, 2020.","title":"Adaptive graph completion based incomplete multi-view clustering","authors":"Wen","year":"2020","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wen et al., 2020b] Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, and Guo-Sen Xie. Cdimc-net: Cog- nitive deep incomplete multi-view clustering network. In International Joint Conference on Artificial Intelligence, pages 3538-3542, 2020.","title":"Cdimc-net: Cognitive deep incomplete multi-view clustering network","authors":"Wen","year":"2020","venue":"International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2020/447"},{"position":28,"raw":"[Wen et al., 2022] Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, and Jinxing Li. A survey on incomplete multiview clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, pages 1136- 1149, 2022.","title":"A survey on incomplete multiview clustering","authors":"Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, Jinxing Li","year":"2022","venue":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wen et al., 2023a] Jie Wen, Chengliang Liu, Shijie Deng, Yicheng Liu, Lunke Fei, Ke Yan, and Yong Xu. Deep double incomplete multi-view multi-label learning with in- complete labels and missing views. IEEE Transactions on Neural Networks and Learning Systems, pages 1-13, 2023.","title":"Deep double incomplete multi-view multi-label learning with incomplete labels and missing views","authors":"Wen","year":"2023","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wen et al., 2023b] Jie Wen, Chengliang Liu, Gehui Xu, Zhihao Wu, Chao Huang, Lunke Fei, and Yong Xu. Highly confident local structure based consensus graph learning for incomplete multi-view clustering. In IEEE Confer- ence on Computer Vision and Pattern Recognition, pages 15712-15721, 2023.","title":"Highly confident local structure based consensus graph learning for incomplete multi-view clustering","authors":"Wen","year":"2023","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wen et al., 2023c] Jie Wen, Gehui Xu, Chengliang Liu, Lunke Fei, Chao Huang, Wei Wang, and Yong Xu. Lo- calized and balanced efficient incomplete multi-view clus- tering. In ACM International Conference on Multimedia, pages 2927-2935, 2023.","title":"Localized and balanced efficient incomplete multi-view clustering","authors":"Wen","year":"2023","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wen et al., 2023d] Jie Wen, Gehui Xu, Zhanyan Tang, Wei Wang, Lunke Fei, and Yong Xu. Graph regularized and feature aware matrix factorization for robust incomplete multi-view clustering. IEEE Transactions on Circuits and Systems for Video Technology, pages 1-14, 2023.","title":"Graph regularized and feature aware matrix factorization for robust incomplete multi-view clustering","authors":"Wen","year":"2023","venue":"IEEE Transactions on Circuits and Systems for Video Technology","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wu et al., 2020] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A com- prehensive survey on graph neural networks. IEEE Trans- actions on Neural Networks and Learning Systems, pages 4-24, 2020.","title":"A comprehensive survey on graph neural networks","authors":"Wu","year":"2020","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xia et al., 2023] Wei Xia, Tianxiu Wang, Quanxue Gao, Ming Yang, and Xinbo Gao. Graph embedding con- trastive multi-modal representation learning for cluster- ing. IEEE Transactions on Image Processing, pages 1170- 1183, 2023.","title":"Graph embedding contrastive multi-modal representation learning for clustering","authors":"Wei Xia, Tianxiu Wang, Quanxue Gao, Ming Yang, Xinbo Gao","year":"2023","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xiao et al., 2023] Shunxin Xiao, Shide Du, Zhaoliang Chen, Yunhe Zhang, and Shiping Wang. Dual fusion- propagation graph neural network for multi-view cluster- ing. IEEE Transactions on Multimedia, pages 1-13, 2023.","title":"Dual fusion-propagation graph neural network for multi-view clustering","authors":"Shunxin Xiao, Shide Du, Zhaoliang Chen, Yunhe Zhang, Shiping Wang","year":"2023","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Xu et al., 2021] Jie Xu, Yazhou Ren, Huayi Tang, Xiaorong Pu, Xiaofeng Zhu, Ming Zeng, and Lifang He. Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering. In IEEE Conference on Computer Vision and Pattern Recognition, pages 9234-9243, 2021.","title":"Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering","authors":"Jie Xu, Yazhou Ren, Huayi Tang, Xiaorong Pu, Xiaofeng Zhu, Ming Zeng, Lifang He","year":"2021","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Xu et al., 2022] Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, and Lifang He. Multi-level feature learning for contrastive multi-view clustering. In IEEE Conference on Computer Vision and Pattern Recognition, pages 16051-16060, 2022.","title":"Multi-level feature learning for contrastive multi-view clustering","authors":"Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, Lifang He","year":"2022","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Xu et al., 2023] Jie Xu, Shuo Chen, Yazhou Ren, Xi- aoshuang Shi, Heng Tao Shen, Gang Niu, and Xiaofeng Zhu. Self-weighted contrastive learning among multiple views for mitigating representation degeneration. In Con- ference on Neural Information Processing Systems, pages 1-13, 2023.","title":"Self-weighted contrastive learning among multiple views for mitigating representation degeneration","authors":"Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Heng Tao Shen, Gang Niu, Xiaofeng Zhu","year":"2023","venue":"Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhan et al., 2019] Kun Zhan, Feiping Nie, Jing Wang, and Yi Yang. Multiview consensus graph clustering. IEEE Transactions on Image Processing, pages 1261-1270, 2019.","title":"Multiview consensus graph clustering","authors":"Feiping Kun Zhan, Jing Nie, Yi Wang, Yang","year":"2019","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhang et al., 2017] Changqing Zhang, Qinghua Hu, Huazhu Fu, Pengfei Zhu, and Xiaochun Cao. Latent multi-view subspace clustering. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4279-4287, 2017.","title":"Latent multi-view subspace clustering","authors":"Changqing Zhang, Qinghua Hu, Huazhu Fu, Pengfei Zhu, Xiaochun Cao","year":"2017","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}