  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.
