[6] Paolo Cignoni, Claudio Montani, and Roberto Scopigno. Dewall: A fast divide and conquer delaunay triangulation algorithm in ed. Computer-Aided Design, 30(5):333-341, 1998.
[7] Amir Egozi, Yosi Keller, and Hugo Guterman. A probabilistic approach to spectral graph matching. IEEE transactions on pattern analysis and machine intelligence, 35(1):18-27, 2012.
[8] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The Pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010.
[9] M. Fey, J. E. Lenssen, C. Morris, J. Masci, and N. M. Kriege. Deep graph matching consensus. In International Conference on Learning Representations (ICLR), 2020.
[10] Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller. Splineconn: Fast geometric deep learning with continuous b-spline kernels. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 869-877, 2018.
[11] Ivan Gonzalez-Diaz, Murat Birinci, Fernando Diaz-de Maria, and Edward J Delp. Neighborhood matching for image retrieval. IEEE Transactions on Multimedia, 19(3):544-558, 2016.
[12] Gregory Griffin, Alex Holub, and Pietro Perona. Caltech-256 object category dataset. 2007.
[13] Zheheng Jiang, Hossein Rahmani, Plamen Angelov, Sue Black, and Bryan M Williams. Graph-context attention networks for size-varied deep graph matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2343-2352, 2022.
[14] Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, and Eduard Trulls. Image matching across wide baselines: From paper to practice. International Journal of Computer Vision, pages 517-547, 2021.
[15] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), 2015.
[16] Zixun Lan, Binjie Hong, Ye Ma, and Fei Ma. More interpretable graph similarity computation via maximum common subgraph inference. arXiv preprint arXiv:2208.04580, 2022.
[17] Marius Leordeanu, Martial Hebert, and Rahul Sukthankar. An integer projected fixed point method for graph matching and map inference. Advances in neural information processing systems, 22, 2009.
[18] Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems, 31, 2018.
[19] Yijie Lin, Mouxing Yang, Jun Yu, Peng Hu, Changqing Zhang, and Xi Peng. Graph matching with bi-level noisy correspondence, 2022.
[20] Zhi-Yong Liu and Hong Qiao. Gnccp—graduated nonconvexity and concavity procedure. IEEE transactions on pattern analysis and machine intelligence, 36(6):1258-1267, 2013.
[21] Gengchen Mai, Krzysztof Janowicz, Bo Yan, Rui Zhu, Ling Cai, and Ni Lao. Multiscale representation learning for spatial feature distributions using grid cells. arXiv preprint arXiv:2003.00824, 2020.
[22] Sohir Maskey, Ali Parviz, Maximilian Thiessen, Hannes Stärk, Ylli Sadikaj, and Haggai Maron. Generalized laplacian positional encoding for graph representation learning. In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations.
[23] Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho. Hyperpixel flow: Semantic correspondence with multi-layer neural features. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3395-3404, 2019.
[24] Hyeonseob Nam and Bohyung Han. Learning multi-domain convolutional neural networks for visual tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4293-4302, 2016.