@inproceedings{3281f855aa5846c88a0260e0ead0dfbe,
title = "An Effective End-to-End Image Matching Network with Attentional Graph Neural Networks",
abstract = "Established feature-based image matching algorithms usually use two networks to obtain the features and matching correspondences of images separately. But then the former network cannot know the results of the operations of the latter network, and backpropagation cannot be performed to optimize the parameters. For the image feature point matching problem, an end-to-end deep learning algorithm is designed. The neural network takes a pair of images as input and obtains feature keypoints, descriptors, and matching correspondences directly after a series of network layers. The advantages of the end-to-end algorithm are verified by comparing the end-to-end training strategies with independence training strategies.",
keywords = "computer vision, deep learning, feature matching, image matching",
author = "Kuiyuan Fu and Zhong Liu and Xingming Wu and Chongshang Sun and Weihai Chen",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022 ; Conference date: 16-12-2022 Through 19-12-2022",
year = "2022",
doi = "10.1109/ICIEA54703.2022.10005924",
language = "英语",
series = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1628--1633",
editor = "Wenxiang Xie and Shibin Gao and Xiaoqiong He and Xing Zhu and Jingjing Huang and Weirong Chen and Lei Ma and Haiyan Shu and Wenping Cao and Lijun Jiang and Zeliang Shu",
booktitle = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",
address = "美国",
}