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Recently, graph convolutional networks (GCNs) have shown great potential for the task of graph matching.
A relationship between arbitrary positive matrices and doubly stochastic matrices
R. Sinkhorn · 1964
Earlier work this paper cites.
An eigenspace projection clustering method for inexact graph matching
T. Caelli and S. Kosinov · 2004
Earlier work this paper cites.
Poselets: Body part detectors trained using 3d human pose annotations
L. Bourdev and J. Malik · 2009
Earlier work this paper cites.
Learning graph matching
T. S. Caetano, J. J. McAuley, L. Cheng, Q. V. Le, and A. J. Smola · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Optimal correspondences from pairwise constraints
O. Enqvist, K. Josephson, and F. Kahl · 2009
Earlier work this paper cites.
Reweighted random walks for graph matching
M. Cho, J. Lee, and K. M. Lee · 2010
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Ranking via sinkhorn propagation
R. P. Adams and R. S. Zemel · 2011
Earlier work this paper cites.
Unsupervised learning for graph matching
M. Leordeanu and M. Hebert · 2012
Earlier work this paper cites.
Graph matching based on spectral embedding with missing value
J. Tang, B. Jiang, A. Zheng, and B. Luo · 2012
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Factorized graph matching
F. Zhou and F. D. la Torre · 2012
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Learning graphs to match
M. Cho, K. Alahari, and J. Ponce · 2013
Cited alongside, same era.
Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
Deeper insights into graph convolutional networks for semi-supervised learning
Q. Li, Z. Han, and X.-M. Wu · 2018
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Revised note on learning quadratic assignment with graph neural networks
A. Nowak, S. Villar, A. S. Bandeira, and J. Bruna · 2018
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Deep learning of graph matching
A. Zanfir and C. Sminchisescu · 2018
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A unified multiple graph learning and convolutional network model for co-saliency estimation
B. Jiang, X. Jiang, A. Zhou, J. Tang, and B. Luo · 2019
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Semi-supervised learning with graph learning-convolutional networks
B. Jiang, Z. Zhang, D. Lin, J. Tang, and B. Luo · 2019
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Graph matching networks for learning the similarity of graph structured objects
Y. Li, C. Gu, T. Dullien, O. Vinyals, and P. Kohli · 2019
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Cited alongside, same era.
A short survey of recent advances in graph matching
J. Yan, X.-C. Yin, W. Lin, C. Deng, H. Zha, and X. Yang · 2016
Cited alongside, same era.
Nonnegative orthogonal graph matching
B. Jiang, J. Tang, C. H. Ding, and B. Luo · 2017
Cited alongside, same era.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
Cited alongside, same era.
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Symmetric graph convolutional autoencoder for unsupervised graph representation learning
J. Park, M. Lee, H. J. Chang, K. Lee, and J. Y. Choi · 2019
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Learning combinatorial embedding networks for deep graph matching
R. Wang, J. Yan, and X. Yang · 2019
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu · 2019
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