We contribute to the sparsely populated area of unsupervised deep graph matching with application to keypoint matching in images. Contrary to the standard \emph{supervised} approach, our method does not require ground truth correspondences between keypoint pairs. Instead, it is self-supervised by enforcing consistency of matchings between images of the same object category. As the matching and the consistency loss are discrete, their derivatives cannot be straightforwardly used for learning. We address this issue in a principled way by building our method upon the recent results on black-box differentiation of combinatorial solvers. This makes our method exceptionally flexible, as it is compatible with arbitrary network architectures and combinatorial solvers. Our experimental evaluation suggests that our technique sets a new state-of-the-art for unsupervised graph matching.

翻译：我们针对图像关键点匹配任务，为稀疏的无监督深度图匹配领域做出了贡献。与标准的监督方法不同，我们的方法不需要关键点对之间的真实对应关系。相反，它通过强制同一物体类别图像之间匹配的一致性来实现自监督。由于匹配和一致性损失是离散的，它们的导数不能直接用于学习。我们以原理性方式解决了这一问题，基于组合求解器黑箱微分的最新成果构建了我们的方法。这使得我们的方法具有极高的灵活性，因为它兼容任意网络架构和组合求解器。我们的实验评估表明，该技术为无监督图匹配设立了新的最优标准。