Knowledge Commons of Institute of Automation,CAS
Proxy Graph Matching with Proximal Matching Networks | |
Tan HR(檀昊儒)1,2![]() ![]() ![]() ![]() ![]() | |
2021-02 | |
会议名称 | The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) |
会议日期 | 2021-2-7 |
会议地点 | 线上远程会议 |
摘要 | Estimating feature point correspondence is a common technique in computer vision. A line of recent data-driven approaches utilizing the graph neural networks improved the matching accuracy by a large margin. However, these learning-based methods require a lot of labeled training data, which are expensive to collect. Moreover, we find most methods are sensitive to global transforms, for example, a random rotation. On the contrary, classical geometric approaches are immune to rotational transformation though their performance is generally inferior. To tackle these issues, we propose a new learning-based matching framework, which is designed to be rotationally invariant. The model only takes geometric information as input. It consists of three parts: a graph neural network to generate a high-level local feature, an attention-based module to normalize the rotational transform, and a global feature matching module based on proximal optimization. To justify our approach, we provide a convergence guarantee for the proximal method for graph matching. The overall performance is validated by numerical experiments. In particular, our approach is trained on the synthetic random graphs and then applied to several real-world datasets. The experimental results demonstrate that our method is robust to rotational transform and highlights its strong performance of matching accuracy. |
关键词 | Graph Matching Combinatorial Optimization Deep Learning |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China[61721004] ; Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China[61836014] |
语种 | 英语 |
七大方向——子方向分类 | 模式识别基础 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45029 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
通讯作者 | Liu CL(刘成林) |
作者单位 | 1.中国科学院自动化研究所模式识别国家重点实验室 2.中国科学院大学人工智能学院 3.中国科学院脑科学与智能技术卓越创新中心 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Tan HR,Wang C,Wu ST,et al. Proxy Graph Matching with Proximal Matching Networks[C],2021. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
3041.TanH.pdf(423KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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