Deep Reinforcement Learning of Graph Matching

• URL: http://arxiv.org/abs/2012.08950v2
• Date: Thu, 18 Mar 2021 07:26:13 GMT
• Title: Deep Reinforcement Learning of Graph Matching
• Authors: Chang Liu, Runzhong Wang, Zetian Jiang, Junchi Yan
• Abstract summary: Graph matching (GM) under node and pairwise constraints has been a building block in areas from optimization to computer vision. We present a reinforcement learning solver for GM i.e. RGM that seeks the node correspondence between pairwise graphs. Our method differs from the previous deep graph matching model in the sense that they are focused on the front-end feature extraction and affinity function learning.
• Score: 63.469961545293756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph matching (GM) under node and pairwise constraints has been a building block in areas from combinatorial optimization, data mining to computer vision, for effective structural representation and association. We present a reinforcement learning solver for GM i.e. RGM that seeks the node correspondence between pairwise graphs, whereby the node embedding model on the association graph is learned to sequentially find the node-to-node matching. Our method differs from the previous deep graph matching model in the sense that they are focused on the front-end feature extraction and affinity function learning, while our method aims to learn the back-end decision making given the affinity objective function whether obtained by learning or not. Such an objective function maximization setting naturally fits with the reinforcement learning mechanism, of which the learning procedure is label-free. These features make it more suitable for practical usage. Extensive experimental results on both synthetic datasets, Willow Object dataset, Pascal VOC dataset, and QAPLIB showcase superior performance regarding both matching accuracy and efficiency. To our best knowledge, this is the first deep reinforcement learning solver for graph matching.

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