1. Introduction
The importance of graph matching (GM) is recognized by its successful applications in machine learning [26], molecule matching [47], and various vision tasks [15], [30], [49]. Existing GM methods mainly follow the optimization formulation by maximizing the node-wise and edgewise affinities of the matched elements, yielding an NP-hard problem known as Quadratic Assignment Problem [22].
Illustrative comparison of injective graph matching (left) and partial graph matching (right) on our remade and released benchmark: imc-pt-sparsegm (originated from imc-pt [18], see sec. 4 for details). Green for correct matches, red for wrong matches. Without partial matching, the gm solver greedily matches all nodes, leading to inferior accuracy. This paper presents a general learning method to mitigate this issue.