QATM: Quality-Aware Template Matching for Deep Learning
Abstract
Finding a template in a search image is one of the core problems in many computer vision applications, such as template matching, image semantic alignment, image-to-GPS verification etc.. In this paper, we propose a novel quality-aware template matching method, which is not only used as a standalone template matching algorithm, but also a trainable layer that can be easily plugged in any deep neural network. Specifically, we assess the quality of a matching pair as its soft-ranking among all matching pairs, and thus different matching scenarios like 1-to-1, 1-to-many, and many-to-many will be all reflected to different values. Our extensive studies in the classic template matching problem and deep learning tasks demonstrate the effectiveness of QATM: it not only outperforms SOTA template matching methods when used alone, but also largely improves existing DNN solutions when used in DNN.
Cite
Text
Cheng et al. "QATM: Quality-Aware Template Matching for Deep Learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.01182Markdown
[Cheng et al. "QATM: Quality-Aware Template Matching for Deep Learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/cheng2019cvpr-qatm/) doi:10.1109/CVPR.2019.01182BibTeX
@inproceedings{cheng2019cvpr-qatm,
title = {{QATM: Quality-Aware Template Matching for Deep Learning}},
author = {Cheng, Jiaxin and Wu, Yue and AbdAlmageed, Wael and Natarajan, Premkumar},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2019},
doi = {10.1109/CVPR.2019.01182},
url = {https://mlanthology.org/cvpr/2019/cheng2019cvpr-qatm/}
}