fix typo & add introduction of GM

chenzhongwang9811 · Jun 29, 2021 · 9ddfe44 · 9ddfe44
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diff --git a/README.md b/README.md
@@ -6,6 +6,28 @@ This repository is developed for the following purposes:
 * **Providing implementation** of state-of-the-art deep graph matching methods.
 * **Benchmarking** existing deep graph matching algorithms under different dataset & experiment settings, for the purpose of fair comparison.
 
+## Introduction to Graph Matching
+Graph Matching (GM) is a fundamental yet challenging problem in computer vision, pattern recognition and data mining. GM aims to find node-to-node correspondence among multiple graphs, by solving an NP-hard combinatorial problem named Quadratic Assignment Problem (QAP). Recently, there is growing interest in developing deep learning based graph matching methods.
+
+Graph matching techniques have been applied to the following applications:
+* [Bridging movie and synopses](https://openaccess.thecvf.com/content_ICCV_2019/papers/Xiong_A_Graph-Based_Framework_to_Bridge_Movies_and_Synopses_ICCV_2019_paper.pdf)
+
+  <img src="images/movie_synopses.png" alt="Bridging movie and synopses, ICCV 2019" width="50%">
+
+* [Image correspondence](https://arxiv.org/pdf/1911.11763.pdf)
+
+  <img src="images/superglue.png" alt="Superglue, CVPR 2020" width="50%">
+
+* [Molecules matching](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Combinatorial_Learning_of_Graph_Edit_Distance_via_Dynamic_Embedding_CVPR_2021_paper.pdf)
+
+  <img src="images/molecules.png" alt="Molecules matching, CVPR 2021" width="50%">
+
+* and more...
+
+Readers are referred to the following survey for more technical details about graph matching:
+* Junchi Yan, Xu-Cheng Yin, Weiyao Lin, Cheng Deng, Hongyuan Zha, Xiaokang Yang. "A Short Survey of Recent Advances in Graph Matching."
+_ICMR 2016_.
+
 ## Deep Graph Matching Algorithms
 _ThinkMatch_ currently contains pytorch source code of the following deep graph matching methods:
 
@@ -15,7 +37,7 @@ _ThinkMatch_ currently contains pytorch source code of the following deep graph
 * [**PCA-GM & IPCA-GM**](/models/PCA)
   * Runzhong Wang, Junchi Yan and Xiaokang Yang. "Combinatorial Learning of Robust Deep Graph Matching: an Embedding based Approach." _TPAMI 2020_.
     [[paper]](https://ieeexplore.ieee.org/abstract/document/9128045/), [[project page]](https://thinklab.sjtu.edu.cn/IPCA_GM.html)
-  * Runzhong Wang, Junchi Yan and Xiaokang Yang. "Learning Combinatorial Embedding Network for Deep Graph Matching." _ICCV 2019_.
+  * Runzhong Wang, Junchi Yan and Xiaokang Yang. "Learning Combinatorial Embedding Networks for Deep Graph Matching." _ICCV 2019_. 
     [[paper]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Combinatorial_Embedding_Networks_for_Deep_Graph_Matching_ICCV_2019_paper.pdf)
 * [**NGM & NGM-v2**](/models/NGM)
   * Runzhong Wang, Junchi Yan, Xiaokang Yang. "Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching." _TPAMI 2021_.

diff --git a/images/molecules.png b/images/molecules.png
diff --git a/images/movie_synopses.png b/images/movie_synopses.png
diff --git a/images/superglue.png b/images/superglue.png
diff --git a/models/PCA/README.md b/models/PCA/README.md
@@ -3,7 +3,7 @@
 This folder contains our implementation of the following papers:
 * Runzhong Wang, Junchi Yan and Xiaokang Yang. "Combinatorial Learning of Robust Deep Graph Matching: an Embedding based Approach." _TPAMI 2020_.
     [[paper]](https://ieeexplore.ieee.org/abstract/document/9128045/), [[project page]](https://thinklab.sjtu.edu.cn/IPCA_GM.html)
-* Runzhong Wang, Junchi Yan and Xiaokang Yang. "Learning Combinatorial Embedding Network for Deep Graph Matching." _ICCV 2019_. 
+* Runzhong Wang, Junchi Yan and Xiaokang Yang. "Learning Combinatorial Embedding Networks for Deep Graph Matching." _ICCV 2019_. 
   [[paper]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Combinatorial_Embedding_Networks_for_Deep_Graph_Matching_ICCV_2019_paper.pdf)