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：我们致力于为数不多的无监督深度图匹配领域做出贡献，并将其应用于图像中的关键点匹配。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，而是通过强制同一物体类别图像之间匹配的一致性进行自监督学习。由于匹配过程及其一致性损失是离散的，其导数无法直接用于学习。我们基于组合求解器黑箱微分的最新成果，以原则性的方式解决了这一问题。这使得我们的方法具有极高的灵活性，能够兼容任意网络架构和组合求解器。实验评估表明，我们的技术为无监督图匹配设立了新的最优标准。