The clustering methods have recently absorbed even-increasing attention in learning and
vision. Deep clustering combines embedding and clustering together to obtain optimal …

Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix,
which can be generally formulated as Lawler's quadratic assignment problem (QAP). This …

Graph matching aims to establish node correspondence between two graphs, which has
been a fundamental problem for its NP-hard nature. One practical consideration is the …

This paper considers the setting of jointly matching and clustering multiple graphs belonging
to different groups, which naturally rises in many realistic problems. Both graph matching …

This paper addresses the problem of multiple graph matching (MGM) by considering both
offline batch mode and online setting. We explore the concept of cycle-consistency over …

Graph matching has received persistent attention over decades, which can be formulated as
a quadratic assignment problem (QAP). We show that a large family of functions, which we …

Graph matching endeavors to find corresponding nodes across two or more graphs, which
plays a fundamental role in many vision and pattern matching tasks. However, existing …

We propose an efficient and robust iterative solution to the multi-object matching problem.
We first clarify serious limitations of current methods as well as the inappropriateness of the …

Finding correspondences between a pair of images is the key ingredient for many
applications such as localization and panorama. However, due to a variety of challenges …

We consider the general setting for partial matching of two or multiple graphs, in the sense
that not necessarily all the nodes in one graph can find their correspondences in another …