Nam Le

Graph Matching (GM)

Nam Le
Table of Contents

Graph Matching (GM) #

Graph Matching is a fundamental combinatorial optimization problem that involves finding correspondences between vertices of two graphs.

Recent Literature #

  1. Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks Arxiv, 2017. paper, code

    Nowak, Alex and Villar, Soledad and Bandeira, S. Afonso and Bruna, Joan

  2. Deep Learning of Graph Matching. CVPR, 2018. paper

    Zanfir, Andrei and Sminchisescu, Cristian

  3. ⭐Learning Combinatorial Embedding Networks for Deep Graph Matching. ICCV, 2019. paper, code

    Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

  4. Deep Graphical Feature Learning for the Feature Matching Problem. ICCV, 2019. paper

    Zhang, Zhen and Lee, Wee Sun

  5. GLMNet: Graph Learning-Matching Networks for Feature Matching. Arxiv, 2019. paper

    Jiang, Bo and Sun, Pengfei and Tang, Jin and Luo, Bin

  6. ⭐Learning deep graph matching with channel-independent embedding and Hungarian attention. ICLR, 2020. paper, code

    Yu, Tianshu and Wang, Runzhong and Yan, Junchi and Li, Baoxin

  7. Deep Graph Matching Consensus. ICLR, 2020. paper

    Fey, Matthias and Lenssen, Jan E. and Morris, Christopher and Masci, Jonathan and Kriege, Nils M.

  8. ⭐Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning. NeurIPS, 2020. paper, code

    Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

  9. ⭐Combinatorial Learning of Robust Deep Graph Matching: An Embedding Based Approach. TPAMI, 2020. paper, code

    Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

  10. Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers. ECCV, 2020. paper, code

    Rolinek, Michal and Swoboda, Paul and Zietlow, Dominik and Paulus, Anselm and Musil, Vit and Martius, Georg

  11. ⭐Neural Graph Matching Network: Learning Lawler’s Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching. TPAMI, 2021. paper, code

    Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

  12. ⭐Deep Latent Graph Matching ICML, 2021. paper

    Yu, Tianshu and Wang, Runzhong and Yan, Junchi and Li, Baoxin.

  13. IA-GM: A Deep Bidirectional Learning Method for Graph Matching AAAI, 2021. paper

    Zhao, Kaixuan and Tu, Shikui and Xu, Lei

  14. Deep Graph Matching under Quadratic Constraint CVPR, 2021. paper

    Gao, Quankai and Wang, Fudong and Xue, Nan and Yu, Jin-Gang and Xia, Gui-Song

  15. GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network MM, 2021. paper

    Jiang, Bo and Sun, Pengfei and Zhang, Ziyan and Tang, Jin and Luo, Bin

  16. Hypergraph Neural Networks for Hypergraph Matching ICCV, 2021. paper

    Liao, Xiaowei and Xu, Yong and Ling, Haibin

  17. Learning to Match Features with Seeded Graph Matching Network ICCV, 2021. paper

    Chen, Hongkai and Luo, Zixin and Zhang, Jiahui and Zhou, Lei and Bai, Xuyang and Hu, Zeyu and Tai, Chiew-Lan and Quan, Long

  18. ⭐Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond CVPR, 2022. paper, code

    Ren, Qibing and Bao, Qingquan and Wang, Runzhong and Yan, Junchi

  19. ⭐Self-supervised Learning of Visual Graph Matching ECCV, 2022. paper, code

    Liu, Chang and Zhang, Shaofeng and Yang, Xiaokang and Yan, Junchi

  20. ⭐Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching. ICLR, 2023. paper, code

    Liu, Chang and Jiang, Zetian and Wang, Runzhong and Yan, Junchi and Huang, Lingxiao and Lu, Pinyan

  21. SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching ICML, 2023. paper

    Yu, Liren and Xu, Jiaming and Lin, Xiaojun

  22. D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching ICML, 2023. paper

    Liu, Xuan, Lin Zhang, Jiaqi Sun, Yujiu Yang and Haiqing Yang

  23. ⭐LinSATNet: The Positive Linear Satisfiability Neural Networks ICML, 2023. paper, code

    Runzhong Wang and Yunhao Zhang and Ziao Guo and Tianyi Chen and Xiaokang Yang and Junchi Yan

  24. LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching NeurIPS, 2023. paper, code

    Nguyen, Duy MH and Nguyen, Hoang and Diep, Nghiem T and Pham, Tan N and Cao, Tri and Nguyen, Binh T and Swoboda, Paul and Ho, Nhat and Albarqouni, Shadi and Xie, Pengtao and others

  25. Improving Graph Matching with Positional Reconstruction Encoder-Decoder Network NeurIPS, 2023. paper

    Zhou, Yixiao and Jia, Ruiqi and Lin, Hongxiang and Quan, Hefeng and Zhao, Yumeng and Lyu, Xiaoqing

  26. Learning to Prune Instances of Steiner Tree Problem in Grap INOC, 2024. paper, code

    Jiwei Zhang, Dena Tayebi, Saurabh Ray, Deepak Ajwani

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