Deep learning methods have demonstrated promising performance on the NP-hard Graph Matching (GM) problems. However, state-of-the-art usually require ground-truth labels, which may take extensive human efforts or be impractical to collect. In this paper, we present a robust self-supervised bidirectional method (IA-SSGM) tackle GM in an unsupervised manner. It involves affinity component and clas...

Recent advances in computer vision, information retrieval, and molecular biology amply demonstrate the value and importance of graph matching. However, graph matching algorithms developed to date are either application domainspecific or severely limited by the problem size. This paper presents a unified solution for the broad range of major graph matching problems. Particularly, the new algorit...