Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc. We propose a novel quality-aware template matching method, QATM, which is not only used as a standalone template matching algorithm, but also a trainable layer that can be easily embedded into any deep neural network. Specifically, we assess the quality of a matching pair using soft-ranking among all matching pairs, and thus different matching scenarios such as 1-to-1, 1-to-many, and many-to-many will be all reflected to different values. Our extensive evaluation on classic template matching benchmarks and deep learning tasks demonstrate the effectiveness of QATM. It not only outperforms state-of-the-art template matching methods when used alone, but also largely improves existing deep network solutions.

本论文介绍了一种新颖的质量感知模板匹配方法，QATM，它不仅可以作为独立的模板匹配算法使用，而且还可以作为可轻松嵌入任何深度神经网络的可训练层。具体而言，通过对所有匹配对进行软排名来评估匹配对的质量，因此不同的匹配情况，例如1对1，1对多和多对多将反映为不同的值。作者的评估表明，QATM 在传统模板匹配基准和深度学习任务上的效果都很好，不仅在单独使用时优于现有的模板匹配方法，而且还大大改善了现有的深度神经网络解决方案。

QATM：面向深度学习的质量感知模板匹配