# Template Matching with Deformable Diversity Similarity

arXiv: https://arxiv.org/abs/1612.02190 · HTML: https://mlsanity.com/paper/1612.02190/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2016.

## Built on by (most cited first, 6 of 6)

- [ContextCLIP: Contextual Alignment of Image-Text pairs on CLIP visual representations](https://mlsanity.com/paper/2211.07122/) (2022)
- [Representing Noisy Image Without Denoising](https://mlsanity.com/paper/2301.07409/) (2023)
- [Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields](https://mlsanity.com/paper/2306.15010/) (2023)
- [Using Keypoint Matching and Interactive Self Attention Network to verify Retail POSMs](https://mlsanity.com/paper/2110.03646/) (2021)
- [Object-Level Targeted Selection via Deep Template Matching](https://mlsanity.com/paper/2207.01778/) (2022)
- [Edge Based Oriented Object Detection](https://mlsanity.com/paper/2309.08265/) (2023)

## Abstract (arXiv)

We propose a novel measure for template matching named Deformable Diversity Similarity -- based on the diversity of feature matches between a target image window and the template. We rely on both local appearance and geometric information that jointly lead to a powerful approach for matching. Our key contribution is a similarity measure, that is robust to complex deformations, significant background clutter, and occlusions. Empirical evaluation on the most up-to-date benchmark shows that our method outperforms the current state-of-the-art in its detection accuracy while improving computational complexity.