{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T04:19:55Z","timestamp":1765340395027,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","funder":[{"name":"National Key Research and Development Program of China","award":["2022ZD0209103"],"award-info":[{"award-number":["2022ZD0209103"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Project 62325604, Project 62276271, Project 62406329, Project 62476281, and Project 62441618"],"award-info":[{"award-number":["Project 62325604, Project 62276271, Project 62406329, Project 62476281, and Project 62441618"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China Joint Found","award":["U24A20323"],"award-info":[{"award-number":["U24A20323"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,27]]},"DOI":"10.1145\/3746027.3754962","type":"proceedings-article","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T06:47:18Z","timestamp":1761374838000},"page":"1102-1111","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DPFMVC: Dynamic Progressive Fusion for Multi-view Clustering"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2780-3718","authenticated-orcid":false,"given":"Taichun","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7829-4924","authenticated-orcid":false,"given":"Zhibin","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9517-262X","authenticated-orcid":false,"given":"Siwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Academy of Military Science, Intelligent Game and Decision Lab, Beijing, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4837-455X","authenticated-orcid":false,"given":"Ke","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7678-687X","authenticated-orcid":false,"given":"Miaomiao","family":"Li","sequence":"additional","affiliation":[{"name":"Changsha University, Changsha, Hunan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9066-1475","authenticated-orcid":false,"given":"Xinwang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5040-3774","authenticated-orcid":false,"given":"En","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5364-5844","authenticated-orcid":false,"given":"Xiangjun","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer, Qilu university of technology, Jinan, Shandong, China"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107852"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01536"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-022-00190-8"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3444269"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2025.3587586"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106361"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3270311"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2025.3535360"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Arthur Gretton Karsten Borgwardt Malte Rasch Bernhard Sch\u00f6lkopf and Alex Smola. 2006. A Kernel Method for The Two-Sample-Problem. In Advances in Neural Information Processing Systems.","DOI":"10.7551\/mitpress\/7503.003.0069"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i16.33861"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-021-00159-z"},{"key":"e_1_3_2_1_12_1","first-page":"504","volume-title":"Reducing the Dimensionality of Data with Neural Networks. Science","volume":"313","author":"Geoffrey","year":"2006","unstructured":"Geoffrey E. Hinton and Ruslan R. Salakhutdinov. 2006. Reducing the Dimensionality of Data with Neural Networks. Science, Vol. 313, 5786 (2006), 504-507."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3335825"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103284"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00740"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01116"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the International Conference on Machine Learning. 17745-17783","author":"Kremer Heiner","year":"2023","unstructured":"Heiner Kremer, Yassine Nemmour, Bernhard Sch\u00f6lkopf, and Jia-Jie Zhu. 2023. Estimation Beyond Data Reweighting: Kernel Method of Moments. In Proceedings of the International Conference on Machine Learning. 17745-17783."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3363217"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17037"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/409"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3582689"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112928"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01102"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3238416"},{"key":"e_1_3_2_1_25_1","first-page":"42369","article-title":"Interactive Deep Clustering via Value Mining","volume":"37","author":"Liu Honglin","year":"2024","unstructured":"Honglin Liu, Peng Hu, Changqing Zhang, Yunfan Li, and Xi Peng. 2024. Interactive Deep Clustering via Value Mining. Advances in Neural Information Processing Systems, Vol. 37 (2024), 42369-42387.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.10.016"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3403155"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2814344"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122151"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00030"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00131"},{"volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 23976-23985","author":"Trosten Daniel J.","key":"e_1_3_2_1_32_1","unstructured":"Daniel J. Trosten, Sigurd L\u00f8kse, Robert Jenssen, and Michael C. Kampffmeyer. 2023. On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 23976-23985."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3502455"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3680915"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29478"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119426"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3246802"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3117403"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/524"},{"key":"e_1_3_2_1_40_1","volume-title":"Proceedings of the International Conference on Machine Learning. 1083-1092","author":"Wang Weiran","year":"2015","unstructured":"Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. 2015. On Deep Multi-View Representation Learning. In Proceedings of the International Conference on Machine Learning. 1083-1092."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681048"},{"key":"e_1_3_2_1_42_1","volume-title":"Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning. In International Conference on Machine Learning. PMLR, 11112-11122","author":"Wen Zixin","year":"2021","unstructured":"Zixin Wen and Yuanzhi Li. 2021. Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning. In International Conference on Machine Learning. PMLR, 11112-11122."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3680677"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3387298"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3094296"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2973981"},{"key":"e_1_3_2_1_47_1","volume-title":"Proceedings of the International Conference on Neural Information Processing Systems. 13","author":"Xu Jie","year":"2023","unstructured":"Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Heng Tao Shen, Gang Niu, and Xiaofeng Zhu. 2023a. Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration. In Proceedings of the International Conference on Neural Information Processing Systems. 13."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3243521"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.111322"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00910"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3193569"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01558"},{"key":"e_1_3_2_1_53_1","volume-title":"Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation. arXiv preprint arXiv:2503.04151","author":"Xu Jie","year":"2025","unstructured":"Jie Xu, Na Zhao, Gang Niu, Masashi Sugiyama, and Xiaofeng Zhu. 2025b. Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation. arXiv preprint arXiv:2503.04151 (2025)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01902"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111448"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00119"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611951"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26285"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6146"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.5555\/3692070.3694438"},{"key":"e_1_3_2_1_61_1","volume-title":"DWCL: Dual-Weighted Contrastive Learning for Multi-View Clustering. arXiv preprint arXiv:2411.17354","author":"Yuan Hanning","year":"2024","unstructured":"Hanning Yuan, Zhihui Zhang, Qi Guo, Lianhua Chi, Sijie Ruan, Jinhui Pang, and Xiaoshuai Hao. 2024. DWCL: Dual-Weighted Contrastive Learning for Multi-View Clustering. arXiv preprint arXiv:2411.17354 (2024)."},{"key":"e_1_3_2_1_62_1","volume-title":"Huazhu Fu, and Qinghua Hu.","author":"Zhang Changqing","year":"2020","unstructured":"Changqing Zhang, Yajie Cui, Zongbo Han, Joey Tianyi Zhou, Huazhu Fu, and Qinghua Hu. 2020. Deep Partial Multi-view Learning. IEEE transactions on pattern analysis and machine intelligence, Vol. 44, 5 (2020), 2402-2415."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00268"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109486"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01463"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681030"}],"event":{"name":"MM '25: The 33rd ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Dublin Ireland","acronym":"MM '25"},"container-title":["Proceedings of the 33rd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746027.3754962","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T04:16:09Z","timestamp":1765340169000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746027.3754962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":66,"alternative-id":["10.1145\/3746027.3754962","10.1145\/3746027"],"URL":"https:\/\/doi.org\/10.1145\/3746027.3754962","relation":{},"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}