# QATM: Quality-Aware Template Matching For Deep Learning

## TL;DR

QATM is a novel, trainable, quality-aware template matching layer that enhances traditional methods and improves deep learning models in various computer vision tasks by assessing match quality through soft-ranking.

## Contribution

Introduces QATM, a flexible, trainable template matching layer that integrates seamlessly into deep networks and considers match quality via soft-ranking, outperforming existing methods.

## Key findings

- Outperforms state-of-the-art template matching methods.
- Significantly improves deep learning solutions for matching tasks.
- Effective across classic benchmarks and deep learning applications.

## Abstract

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.