# Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields

arXiv: https://arxiv.org/abs/2306.15010 · HTML: https://mlsanity.com/paper/2306.15010/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation](https://mlsanity.com/paper/1805.04574/) (2018)
- [ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms](https://mlsanity.com/paper/1807.05614/) (2018)
- [Best-Buddies Similarity - Robust Template Matching using Mutual Nearest Neighbors](https://mlsanity.com/paper/1609.01571/) (2016)
- [Long-Term Visual Object Tracking Benchmark](https://mlsanity.com/paper/1712.01358/) (2017)
- [QATM: Quality-Aware Template Matching For Deep Learning](https://mlsanity.com/paper/1903.07254/) (2019)
- [Approximate Nearest Neighbor Fields in Video](https://mlsanity.com/paper/1508.07953/) (2015)
- [Template Matching with Deformable Diversity Similarity](https://mlsanity.com/paper/1612.02190/) (2016)
- [Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling](https://mlsanity.com/paper/1410.7484/) (2014)
- [OATM: Occlusion Aware Template Matching by Consensus Set Maximization](https://mlsanity.com/paper/1804.02638/) (2018)
- [Explaining Away Results in Accurate and Tolerant Template Matching](https://mlsanity.com/paper/1911.04169/) (2019)

## Built on by (most cited first, 1 of 1)

- [An Efficient Deep Template Matching and In-Plane Pose Estimation Method via Template-Aware Dynamic Convolution](https://mlsanity.com/paper/2510.01678/) (2025)

## Abstract (arXiv)

Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest-neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pixel with its NN in the template. NN-based methods have been shown to perform better in occlusions, appearance changes, and non-rigid transformations; however, they scale poorly with high-resolution data and high feature dimensions. We present an NN-based method which efficiently reduces the NN computations and introduces filtering in the NN fields (NNFs). A vector quantization step is introduced before the NN calculation to represent the template with k features, and the filter response over the NNFs is used to compare the template and query distributions over the features. We show that state-of-the-art performance is achieved in low-resolution data, and our method outperforms previous methods at higher resolution.