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 advantage 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 \(1\,\times \,1\) 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 that the model is applicable 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.

图匹配是计算机视觉和模式识别中常用的技术。最近的数据驱动方法显着提高了图匹配精度，而一些传统的基于算法的方法对特征噪声、异常节点和全局变换（例如旋转）更加鲁棒。在本文中，我们提出了一种基于图神经网络（GNN）的方法，结合了数据驱动和传统方法的优点。在GNN框架中，我们将传统的图匹配求解器转变为关联图上的单通道GNN，并将单通道架构扩展到多通道网络。所提出的模型可以看作是一种在每次迭代中融合多种算法的集成方法。在我们的方法中，集成算法的独立迭代不是在集成结束时对估计进行平均，而是在每次迭代后通过\(1\,\times \,1\)通道卷积层交换信息。实验表明，我们的模型显着提高了传统算法的性能。此外，我们提出了随机采样策略来降低计算复杂度和GPU内存使用量，使该模型适用于具有数千个节点的图匹配。我们评估了我们的方法在三个任务上的性能：几何图匹配、语义特征匹配和少样本 3D 形状分类。所提出的模型的性能与现有最好的基于 GNN 的方法相当或优于。