# QATM: Quality-Aware Template Matching For Deep Learning

arXiv: https://arxiv.org/abs/1903.07254 · HTML: https://mlsanity.com/paper/1903.07254/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 10 of 10)

- [U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization](https://mlsanity.com/paper/2310.13766/) (2023)
- [Learning Accurate Template Matching with Differentiable Coarse-to-Fine Correspondence Refinement](https://mlsanity.com/paper/2303.08438/) (2023)
- [Object-aware graph matching network for cross-domain remote sensing image localization](https://mlsanity.com/paper/2511.02489/) (2025)
- [Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields](https://mlsanity.com/paper/2306.15010/) (2023)
- [Learn to Differ: Sim2Real Small Defection Segmentation Network](https://mlsanity.com/paper/2103.04297/) (2021)
- [Object-Level Targeted Selection via Deep Template Matching](https://mlsanity.com/paper/2207.01778/) (2022)
- [One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text](https://mlsanity.com/paper/2209.06584/) (2022)
- [An Efficient Deep Template Matching and In-Plane Pose Estimation Method via Template-Aware Dynamic Convolution](https://mlsanity.com/paper/2510.01678/) (2025)
- [BioTamperNet: Affinity-Guided State-Space Model Detecting Tampered Biomedical Images](https://mlsanity.com/paper/2602.01435/) (2026)
- [Can Image Splicing and Copy-Move Forgery Be Detected by the Same Model? Forensim: An Attention-Based State-Space Approach](https://mlsanity.com/paper/2602.10079/) (2026)

## Abstract (arXiv)

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.