  Graph matching is a commonly used technique in computer vision and pattern
recognition. Recent data-driven approaches have improved the graph matching
accuracy remarkably, whereas some traditional algorithm-based methods are more
robust to feature noises, outlier nodes, and global transformation
(e.g.~rotation). In this paper, we propose a graph neural network (GNN) based
approach to combine the advantages of data-driven and traditional methods. In
the GNN framework, we transform traditional graph-matching solvers as
single-channel GNNs on the association graph and extend the single-channel
architecture to the multi-channel network. The proposed model can be seen as an
ensemble method that fuses multiple algorithms at every iteration. Instead of
averaging the estimates at the end of the ensemble, in our approach, the
independent iterations of the ensembled algorithms exchange their information
after each iteration via a 1x1 channel-wise convolution layer. Experiments show
that our model improves the performance of traditional algorithms
significantly. In addition, we propose a random sampling strategy to reduce the
computational complexity and GPU memory usage, so the model applies to matching
graphs with thousands of nodes. We evaluate the performance of our method on
three tasks: geometric graph matching, semantic feature matching, and few-shot
3D shape classification. The proposed model performs comparably or outperforms
the best existing GNN-based methods.
