{"type":"ScholarlyArticle","title":"Integrating Vision-Language Semantic Graphs in Multi-View Clustering","authors":[{"name":"JunLong Ke","slug":"junlong-ke","given":"JunLong","family":"Ke","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Zichen Wen","slug":"zichen-wen","given":"Zichen","family":"Wen","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Yechenhao Yang","slug":"yechenhao-yang","given":"Yechenhao","family":"Yang","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Chenhang Cui","slug":"chenhang-cui","given":"Chenhang","family":"Cui","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Yazhou Ren","slug":"yazhou-ren","given":"Yazhou","family":"Ren","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China; Shenzhen Institute for Advanced Study , University of Electronic Science and Technology of China"},{"name":"Xiaorong Pu","slug":"xiaorong-pu","given":"Xiaorong","family":"Pu","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China; Shenzhen Institute for Advanced Study , University of Electronic Science and Technology of China"},{"name":"Lifang He","slug":"lifang-he","given":"Lifang","family":"He","affiliation":"Department of Computer Science and Engineering , Lehigh University"}],"abstract":"In recent years, a variety of graph learning-based multi-view clustering (MVC) methods have emerged. However, these methods continue to face challenges in extracting latent features from real-world data, particularly in scenarios involving high-resolution color images and high-dimensional features. This task is notably difficult in cases where images are visually similar yet semantically diverse. To address this issue, we present a novel large-scale pre-trained model for multi-view clustering, named Integrate Vision-Language Semantic Graphs in Multi-View Clustering (IVSGMV), which harnesses the capabilities of visual-language pre-training models to enhance clustering performance and confronts issues in the unsupervised tuning of pre-trained models for multi-view data. We introduce an effective unsupervised approach for creating semantic graphs from image multi-view datasets using pre-trained encoders. Our method addresses the inherent spatial noise and imbalance in these encoders by employing graph filters and a joint process that integrates both image node and edge features. Additionally, we demonstrate the application of our approach to multi-view image clustering on extensive datasets, notably the high-resolution MVImgNet, achieving an impressive 82% accuracy. Furthermore, our method extends the zero-shot capabilities of large-scale pre-trained models, resulting in good performance in clustering tasks on untrained multi-view datasets.","doi":"10.24963/ijcai.2024/472","url":"https://ijcai.org/proceedings/2024/472","pdf":"https://ijcai.org/proceedings/2024/0472.pdf","markdown":"https://ijcai.org/proceedings/2024/472.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/472","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":472,"pages":{"first":4273,"last":4281},"keywords":["Machine Learning: ML: Multi-modal learning","Machine Learning: ML: Clustering","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Adaloglou et al., 2023] Nikolas Adaloglou, Felix Michels, Hamza Kalisch, and Markus Kollmann. Exploring the lim- its of deep image clustering using pretrained models. arXiv preprint arXiv:2303.17896, 2023.","title":"Exploring the limits of deep image clustering using pretrained models","authors":"Nikolas Adaloglou, Felix Michels, Hamza Kalisch, Markus Kollmann","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bo et al., 2021] Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen. Beyond low-frequency information in graph convolutional networks. In AAAI, pages 3950-3957, 2021.","title":"Beyond low-frequency information in graph convolutional networks","authors":"Deyu Bo, Xiao Wang, Chuan Shi, Huawei Shen","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cai et al., 2023] Shaotian Cai, Liping Qiu, Xiaojun Chen, Qin Zhang, and Longteng Chen. Semantic-enhanced im- age clustering. In AAAI, pages 6869-6878, 2023.","title":"Semantic-enhanced image clustering","authors":"Shaotian Cai, Liping Qiu, Xiaojun Chen, Qin Zhang, Longteng Chen","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cui et al., 2023] Chenhang Cui, Yazhou Ren, Jingyu Pu, Xiaorong Pu, and Lifang He. Deep multi-view subspace clustering with anchor graph. In IJCAI, pages 3577-3585, 2023.","title":"Deep multi-view subspace clustering with anchor graph","authors":"Cui","year":"2023","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2023/398"},{"position":5,"raw":"[Dong et al., 2023] Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, and Jun Zhu. How robust is google's bard to ad- versarial image attacks? arXiv preprint arXiv:2309.11751, 2023.","title":"How robust is google's bard to adversarial image attacks","authors":"Dong","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dosovitskiy et al., 2020] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Min- derer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.","title":"An image is worth 16x16 words: Transformers for image recognition at scale","authors":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Guan et al., 2024a] Renxiang Guan, Zihao Li, Xianju Li, and Chang Tang. Pixel-superpixel contrastive learning and pseudo-label correction for hyperspectral image cluster- ing. In ICASSP, pages 6795-6799, 2024.","title":"Pixel-superpixel contrastive learning and pseudo-label correction for hyperspectral image clustering","authors":"Guan","year":"2024","venue":"ICASSP","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Guan et al., 2024b] Renxiang Guan, Zihao Li, Wenxuan Tu, Jun Wang, Yue Liu, Xianju Li, Chang Tang, and Ruyi Feng. Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional net- works. TGRS, 62:1-14, 2024.","title":"Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional networks","authors":"Guan","year":"2024","venue":"TGRS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Higgins et al., 2017] Irina Higgins, Loic Matthey, Arka Pal, Christopher P Burgess, Xavier Glorot, Matthew M Botvinick, Shakir Mohamed, and Alexander Lerchner. beta-vae: Learning basic visual concepts with a con- strained variational framework. In ICLR, 2017.","title":"beta-vae: Learning basic visual concepts with a constrained variational framework","authors":"Irina Higgins, Loic Matthey, Arka Pal, P Christopher, Xavier Burgess, Matthew M Glorot, Shakir Botvinick, Alexander Mohamed, Lerchner","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jiang et al., 2017] Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou. Variational deep embedding: an unsupervised and generative approach to clustering. In IJCAI, pages 1965-1972, 2017.","title":"Variational deep embedding: an unsupervised and generative approach to clustering","authors":"Jiang","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/273"},{"position":11,"raw":"[Kullback and Leibler, 1951] Solomon Kullback and Richard A Leibler. On information and sufficiency. The annals of mathematical statistics, 22(1):79-86, 1951.","title":"On information and sufficiency","authors":"Leibler ; A Kullback, Leibler","year":"1951","venue":"The annals of mathematical statistics","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[LeCun et al., 1998] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning ap- plied to document recognition. Proceedings of the IEEE, 86:2278-2324, 1998.","title":"Gradient-based learning applied to document recognition","authors":"Lecun","year":"1998","venue":"Proceedings of the IEEE","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2019a] Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. Visualbert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557, 2019.","title":"Visualbert: A simple and performant baseline for vision and language","authors":"Li","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Li et al., 2019b] Ruihuang Li, Changqing Zhang, Huazhu Fu, Xi Peng, Tianyi Zhou, and Qinghua Hu. Reciprocal multi-layer subspace learning for multi-view clustering. In ICCV, pages 8172-8180, 2019.","title":"Reciprocal multi-layer subspace learning for multi-view clustering","authors":"Li","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2020] Gen Li, Nan Duan, Yuejian Fang, Ming Gong, and Daxin Jiang. Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training. In AAAI, pages 11336-11344, 2020.","title":"Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training","authors":"Gen Li, Nan Duan, Yuejian Fang, Ming Gong, Daxin Jiang","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Ling et al., 2023] Yawen Ling, Jianpeng Chen, Yazhou Ren, Xiaorong Pu, Jie Xu, Xiaofeng Zhu, and Lifang He. Dual label-guided graph refinement for multi-view graph clustering. In AAAI, pages 8791-8798, 2023.","title":"Dual label-guided graph refinement for multi-view graph clustering","authors":"Yawen Ling, Jianpeng Chen, Yazhou Ren, Xiaorong Pu, Jie Xu, Xiaofeng Zhu, Lifang He","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[MacQueen, 1967] James MacQueen. Classification and analysis of multivariate observations. In BSMSP, pages 281-297, 1967.","title":"Classification and analysis of multivariate observations","authors":"James Macqueen","year":"1967","venue":"BSMSP","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Miller, 1995] George A Miller. Wordnet: a lexical database for english. CACM, 38(11):39-41, 1995.","title":"Wordnet: a lexical database for english","authors":"George A Miller","year":"1995","venue":"CACM","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Nene et al., 1996] Sameer A Nene, Shree K Nayar, Hiroshi Murase, et al. Columbia object image library (coil-20). Technical Report, CUCS-005-96, 1996.","title":"Columbia object image library (coil-20)","authors":"Sameer A Nene, Hiroshi Shree K Nayar, Murase","year":"1996","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Nt and Maehara, 2019] Hoang Nt and Takanori Maehara. Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550, 2019.","title":"Revisiting graph neural networks: All we have is low-pass filters","authors":"Maehara Nt, Hoang Nt, Takanori Maehara","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Radford et al., 2021] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from nat- ural language supervision. In ICML, pages 8748-8763, 2021.","title":"Learning transferable visual models from natural language supervision","authors":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Ramesh et al., 2021] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In ICML, pages 8821-8831, 2021.","title":"Zero-shot text-to-image generation","authors":"Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ren et al., 2020] Yazhou Ren, Shudong Huang, Peng Zhao, Minghao Han, and Zenglin Xu. Self-paced and auto-weighted multi-view clustering. Neurocomputing, 383:248-256, 2020.","title":"Self-paced and auto-weighted multi-view clustering","authors":"Shudong Yazhou Ren, Peng Huang, Minghao Zhao, Zenglin Han, Xu","year":"2020","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Ren et al., 2022] Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S Yu, and Li- fang He. Deep clustering: A comprehensive survey. arXiv preprint arXiv:2210.04142, 2022.","title":"Deep clustering: A comprehensive survey","authors":"Jingyu Yazhou Ren, Zhimeng Pu, Jie Yang, Guofeng Xu, Xiaorong Li, Philip S Pu, Lifang Yu, He","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ren et al., 2024] Yazhou Ren, Xinyue Chen, Jie Xu, Jingyu Pu, Yonghao Huang, Xiaorong Pu, Ce Zhu, Xiaofeng Zhu, Zhifeng Hao, and Lifang He. A novel federated multi- view clustering method for unaligned and incomplete data fusion. Information Fusion, 108:102357, 2024.","title":"A novel federated multiview clustering method for unaligned and incomplete data fusion","authors":"Xinyue Yazhou Ren, Jie Chen, Jingyu Xu, Yonghao Pu, Xiaorong Huang, Ce Pu, Xiaofeng Zhu, Zhifeng Zhu, Lifang Hao, He","year":"2024","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Shen et al., 2023] Shuai Shen, Wanhua Li, Xiaobing Wang, Dafeng Zhang, Zhezhu Jin, Jie Zhou, and Jiwen Lu. Clip- cluster: Clip-guided attribute hallucination for face clus- tering. In CVPR, pages 20786-20795, 2023.","title":"Clipcluster: Clip-guided attribute hallucination for face clustering","authors":"Shuai Shen, Wanhua Li, Xiaobing Wang, Dafeng Zhang, Zhezhu Jin, Jie Zhou, Jiwen Lu","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Van Gansbeke et al., 2020] Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool. Scan: Learning to classify images without labels. In ECCV, pages 268-285, 2020.","title":"Scan: Learning to classify images without labels","authors":"Van Gansbeke","year":"2020","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2021] Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, and Yun Fu. Generative partial multi-view clustering with adaptive fusion and cycle consistency. TIP, 30:1771-1783, 2021.","title":"Generative partial multi-view clustering with adaptive fusion and cycle consistency","authors":"Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, Yun Fu","year":"2021","venue":"TIP","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2022] Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, and En Zhu. Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences. In NeurIPS, pages 5882-5895, 2022.","title":"Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences","authors":"Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, En Zhu","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2023] Qianqian Wang, Zhiqiang Tao, Wei Xia, Quanxue Gao, Xiaochun Cao, and Licheng Jiao. Adver- sarial multiview clustering networks with adaptive fusion. TNNLS, 34:7635-7647, 2023.","title":"Adversarial multiview clustering networks with adaptive fusion","authors":"Qianqian Wang, Zhiqiang Tao, Wei Xia, Quanxue Gao, Xiaochun Cao, Licheng Jiao","year":"2023","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wen et al., 2024] Zichen Wen, Yawen Ling, Yazhou Ren, Tianyi Wu, Jianpeng Chen, Xiaorong Pu, Zhifeng Hao, and Lifang He. Homophily-related: Adaptive hybrid graph filter for multi-view graph clustering. In AAAI, pages 15841-15849, 2024.","title":"Homophily-related: Adaptive hybrid graph filter for multi-view graph clustering","authors":"Wen","year":"2024","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wu et al., 2024] Song Wu, Yan Zheng, Yazhou Ren, Jing He, Xiaorong Pu, Shudong Huang, Zhifeng Hao, and Li- fang He. Self-weighted contrastive fusion for deep multi- view clustering. TMM, 2024.","title":"Self-weighted contrastive fusion for deep multiview clustering","authors":"Song Wu, Yan Zheng, Yazhou Ren, Jing He, Xiaorong Pu, Shudong Huang, Zhifeng Hao, Lifang He","year":"2024","venue":"TMM","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xiao et al., 2017] Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for bench- marking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017.","title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","authors":"Han Xiao, Kashif Rasul, Roland Vollgraf","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xu and Wei, 2021] Yuanjin Xu and Ming Wei. Multi-view clustering toward aerial images by combining spectral analysis and local refinement. Future Generation Com- puter Systems, 117:138-144, 2021.","title":"Multi-view clustering toward aerial images by combining spectral analysis and local refinement","authors":"Wei ; ] Yuanjin Xu, Ming Xu, Wei","year":"2021","venue":"Future Generation Computer Systems","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xu et al., 2021a] Jie Xu, Yazhou Ren, Guofeng Li, Lili Pan, Ce Zhu, and Zenglin Xu. Deep embedded multi-view clus- tering with collaborative training. Information Sciences, 573:279-290, 2021.","title":"Deep embedded multi-view clustering with collaborative training","authors":"Xu","year":"2021","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Xu et al., 2021b] Jie Xu, Yazhou Ren, Huayi Tang, Xi- aorong Pu, Xiaofeng Zhu, Ming Zeng, and Lifang He. Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clus- tering. In ICCV, pages 9234-9243, 2021.","title":"Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering","authors":"Xu","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Xu et al., 2022a] Jie Xu, Chao Li, Yazhou Ren, Liang Peng, Yujie Mo, Xiaoshuang Shi, and Xiaofeng Zhu. Deep in- complete multi-view clustering via mining cluster comple- mentarity. In AAAI, pages 8761-8769, 2022.","title":"Deep incomplete multi-view clustering via mining cluster complementarity","authors":"Xu","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Xu et al., 2022b] Jie Xu, Yazhou Ren, Huayi Tang, Zhi- meng Yang, Lili Pan, Yang Yang, Xiaorong Pu, Philip S Yu, and Lifang He. Self-supervised discriminative feature learning for deep multi-view clustering. TKDE, 2022.","title":"Self-supervised discriminative feature learning for deep multi-view clustering","authors":"Xu","year":"2022","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Xu et al., 2022c] Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, and Lifang He. Multi-level feature learning for contrastive multi-view clustering. In CVPR, pages 16051-16060, 2022.","title":"Multi-level feature learning for contrastive multi-view clustering","authors":"Xu","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Yan et al., 2023] Weiqing Yan, Yuanyang Zhang, Chenlei Lv, Chang Tang, Guanghui Yue, Liang Liao, and Weisi Lin. Gcfagg: Global and cross-view feature aggregation for multi-view clustering. In CVPR, pages 19863-19872, 2023.","title":"Gcfagg: Global and cross-view feature aggregation for multi-view clustering","authors":"Weiqing Yan, Yuanyang Zhang, Chenlei Lv, Chang Tang, Guanghui Yue, Liang Liao, Weisi Lin","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yan et al., 2024] Wenbiao Yan, Yiyang Zhou, Yifei Wang, Qinghai Zheng, and Jihua Zhu. Multi-view semantic consistency based information bottleneck for clustering. Knowledge-Based Systems, 288:111448, 2024.","title":"Multi-view semantic consistency based information bottleneck for clustering","authors":"Yan","year":"2024","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yang et al., 2017] Bo Yang, Xiao Fu, Nicholas D Sidiropoulos, and Mingyi Hong. Towards k-means- friendly spaces: Simultaneous deep learning and clustering. In ICML, pages 3861-3870, 2017.","title":"Towards k-meansfriendly spaces: Simultaneous deep learning and clustering","authors":"Bo Yang, Xiao Fu, Nicholas D Sidiropoulos, Mingyi Hong","year":"2017","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Yu et al., 2023] Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, et al. Mvimgnet: A large-scale dataset of multi-view images. In CVPR, pages 9150-9161, 2023.","title":"Mvimgnet: A large-scale dataset of multi-view images","authors":"Yu","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2015] Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, and Xiaochun Cao. Low-rank tensor con- strained multiview subspace clustering. In ICCV, pages 1582-1590, 2015.","title":"Low-rank tensor constrained multiview subspace clustering","authors":"Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, Xiaochun Cao","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2018a] Changqing Zhang, Huazhu Fu, Qinghua Hu, Xiaochun Cao, Yuan Xie, Dacheng Tao, and Dong Xu. Generalized latent multi-view subspace clustering. TPAMI, 42(1):86-99, 2018.","title":"Generalized latent multi-view subspace clustering","authors":"Zhang","year":"2018","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhang et al., 2018b] Zheng Zhang, Li Liu, Fumin Shen, Heng Tao Shen, and Ling Shao. Binary multi-view clus- tering. TPAMI, 41:1774-1782, 2018.","title":"Binary multi-view clustering","authors":"Zhang","year":"2018","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhao et al., 2021] Han Zhao, Xu Yang, Zhenru Wang, Erkun Yang, and Cheng Deng. Graph debiased contrastive learning with joint representation clustering. In IJCAI, pages 3434-3440, 2021.","title":"Graph debiased contrastive learning with joint representation clustering","authors":"Han Zhao, Xu Yang, Zhenru Wang, Erkun Yang, Cheng Deng","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2021/473"},{"position":48,"raw":"[Zhou et al., 2020] Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. Graph neural networks: A review of methods and applications. AI open, 1:57-81, 2020.","title":"Graph neural networks: A review of methods and applications","authors":"Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun","year":"2020","venue":"AI open","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Zhou et al., 2023] Yiyang Zhou, Qinghai Zheng, Shunshun Bai, and Jihua Zhu. Semantically consistent multi- view representation learning. Knowledge-Based Systems, 278:110899, 2023.","title":"Semantically consistent multiview representation learning","authors":"Yiyang Zhou, Qinghai Zheng, Shunshun Bai, Jihua Zhu","year":"2023","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Zhou et al., 2024a] Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, and Huaxiu Yao. Analyzing and mitigating object hallucination in large vision-language models. In ICLR, 2024.","title":"Analyzing and mitigating object hallucination in large vision-language models","authors":"Zhou","year":"2024","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Zhou et al., 2024b] Yiyang Zhou, Qinghai Zheng, Yifei Wang, Wenbiao Yan, Pengcheng Shi, and Jihua Zhu. Mcoco: Multi-level consistency collaborative multi-view clustering. Expert Systems with Applications, 2024.","title":"Mcoco: Multi-level consistency collaborative multi-view clustering","authors":"Zhou","year":"2024","venue":"Expert Systems with Applications","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}