jinjiaqi1998/Awesome-Deep-Multi-View-Clustering
Awesome Deep Multi-View Clustering is a collection of SOTA, novel deep multi-view clustering methods (papers and codes).
Awesome-Deep-Multi-View-Clustering
Collections for state-of-the-art and novel deep neural network-based multi-view clustering approaches (papers & codes). According to the integrity of multi-view data, such methods can be further subdivided into Deep Multi-view Clustering(DMVC) and Deep Incomplete Multi-view Clustering(DIMVC).
We are looking forward for other participants to share their papers and codes. If interested or any question about the listed papers and codes, please contact jinjiaqi@nudt.edu.cn. If you find this repository useful to your research or work, it is really appreciated to star this repository. ✨ If you use our code or the processed datasets in this repository for your research, please cite 1-2 papers in the citation part here. ❤️
Table of Contents
What's Deep Multi-view Clustering?
Deep multi-view clustering aims to reveal the potential complementary information of multiple features or modalities through deep neural networks, and finally divide samples into different groups in unsupervised scenarios.
Surveys
Papers & Codes
According to the integrity of multi-view data, the paper is divided into deep multi-view clustering methods and deep incomplete multi-view clustering approaches.
Deep Multi-view Clustering(DMVC)
Deep Incomplete Multi-view Clustering(DIMVC)
Citation
@inproceedings{jin2023deep,
title={Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment},
author={Jin, Jiaqi and Wang, Siwei and Dong, Zhibin and Liu, Xinwang and Zhu, En},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={11600--11609},
year={2023}
}
@inproceedings{wangevaluate,
title={Evaluate then Cooperate: Shapley-based View Cooperation Enhancement for Multi-view Clustering},
author={Wang, Fangdi and Jin, Jiaqi and Hu, Jingtao and Liu, Suyuan and Yang, Xihong and Wang, Siwei and Liu, Xinwang and Zhu, En},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}
}
@article{wang2022align,
title={Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences},
author={Wang, Siwei and Liu, Xinwang and Liu, Suyuan and Jin, Jiaqi and Tu, Wenxuan and Zhu, Xinzhong and Zhu, En},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={5882--5895},
year={2022}
}
@inproceedings{dong2023cross,
title={Cross-view topology based consistent and complementary information for deep multi-view clustering},
author={Dong, Zhibin and Wang, Siwei and Jin, Jiaqi and Liu, Xinwang and Zhu, En},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={19440--19451},
year={2023}
}
@inproceedings{yang2023dealmvc,
title={Dealmvc: Dual contrastive calibration for multi-view clustering},
author={Yang, Xihong and Jiaqi, Jin and Wang, Siwei and Liang, Ke and Liu, Yue and Wen, Yi and Liu, Suyuan and Zhou, Sihang and Liu, Xinwang and Zhu, En},
booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
pages={337--346},
year={2023}
}
@inproceedings{wang2024view,
title={View Gap Matters: Cross-view Topology and Information Decoupling for Multi-view Clustering},
author={Wang, Fangdi and Jin, Jiaqi and Dong, Zhibin and Yang, Xihong and Feng, Yu and Liu, Xinwang and Zhu, Xinzhong and Wang, Siwei and Liu, Tianrui and Zhu, En},
booktitle={Proceedings of the 32nd ACM International Conference on Multimedia},
pages={8431--8440},
year={2024}
}
@article{dong2024subgraph,
title={Subgraph Propagation and Contrastive Calibration for Incomplete Multiview Data Clustering},
author={Dong, Zhibin and Jin, Jiaqi and Xiao, Yuyang and Xiao, Bin and Wang, Siwei and Liu, Xinwang and Zhu, En},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2024},
publisher={IEEE}
}
@article{dong2023iterative,
title={Iterative deep structural graph contrast clustering for multiview raw data},
author={Dong, Zhibin and Jin, Jiaqi and Xiao, Yuyang and Wang, Siwei and Zhu, Xinzhong and Liu, Xinwang and Zhu, En},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2023},
publisher={IEEE}
}
