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Learning-based approaches to graph matching have been developed and explored for more than a decade, and have grown rapidly in scope and popularity recently. However, previous learning-based algorithms, with or without deep learning strategy, mainly focus on the learning of node and/or edge affinities generation, and pay less attention to the learning of the combinatorial solver. In this paper we propose a fully trainable framework for graph matching, in which learning of affinities and solvingdoi:10.1109/cvpr42600.2020.00759 dblp:conf/cvpr/WangLLJHL20 fatcat:dp5itqktsrcu7lnypwe6rd5jhy