Learnable Graph Matching: A Practical Paradigm for Data Association

• URL: http://arxiv.org/abs/2303.15414v2
• Date: Tue, 6 Feb 2024 10:24:49 GMT
• Title: Learnable Graph Matching: A Practical Paradigm for Data Association
• Authors: Jiawei He, Zehao Huang, Naiyan Wang, Zhaoxiang Zhang
• Abstract summary: We propose a general learnable graph matching method to address these issues. Our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet.
• Score: 74.28753343714858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. however, current data association solutions have some defects: they mostly ignore the intra-view context information; besides, they either train deep association models in an end-to-end way and hardly utilize the advantage of optimization-based assignment methods, or only use an off-the-shelf neural network to extract features. In this paper, we propose a general learnable graph matching method to address these issues. Especially, we model the intra-view relationships as an undirected graph. Then data association turns into a general graph matching problem between graphs. Furthermore, to make optimization end-to-end differentiable, we relax the original graph matching problem into continuous quadratic programming and then incorporate training into a deep graph neural network with KKT conditions and implicit function theorem. In MOT task, our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet. For point cloud registration, we also achieve competitive results. Code will be available at https://github.com/jiaweihe1996/GMTracker.

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