Graph matching is an important problem that has received widespread attention, especially in the field of computer vision. Recently, state-of-the-art methods seek to incorporate graph matching with deep learning. However, there is no research to explain what role the graph matching algorithm plays in the model. Therefore, we propose an approach integrating a MILP formulation of the graph matching problem. This formulation is solved to optimal and it provides inherent baseline. Meanwhile, similar approaches are derived by releasing the optimal guarantee of the graph matching solver and by introducing a quality level. This quality level controls the quality of the solutions provided by the graph matching solver. In addition, several relaxations of the graph matching problem are put to the test. Our experimental evaluation gives several theoretical insights and guides the direction of deep graph matching methods.
翻译：图表匹配是一个受到广泛关注的重要问题,特别是在计算机视觉领域。最近,最先进的方法试图将图表与深层学习相匹配。然而,目前没有研究来解释图形匹配算法在模型中扮演什么角色。因此,我们建议采用一个方法,将图形匹配问题的MILP配法组合在一起。这一配法可以最佳地解决,并提供内在基线。与此同时,通过释放图形匹配求解器的最佳保证和引入质量水平,也得出了类似的方法。这种质量水平控制了图形匹配求解器所提供的解决方案的质量。此外,对图形匹配算法问题的一些放松被置于测试之中。我们的实验性评估提供了一些理论见解,指导了深图匹配方法的方向。
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