# Attention for Image Registration (AiR): an unsupervised Transformer approach

arXiv: https://arxiv.org/abs/2105.02282 · HTML: https://mlsanity.com/paper/2105.02282/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [Depth-Adaptive Transformer](https://mlsanity.com/paper/1910.10073/) (2019)
- [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018)
- [Fast Transformers with Clustered Attention](https://mlsanity.com/paper/2007.04825/) (2020)
- [Learning a Probabilistic Model for Diffeomorphic Registration](https://mlsanity.com/paper/1812.07460/) (2018)
- [Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks](https://mlsanity.com/paper/2104.12510/) (2021)

## Built on by (most cited first, 2 of 2)

- [PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion](https://mlsanity.com/paper/2107.13967/) (2021)
- [A Two-Step Deep Learning Method for 3DCT-2DUS Kidney Registration During Breathing](https://mlsanity.com/paper/2305.13855/) (2023)

## Abstract (arXiv)

Image registration is a crucial task in signal processing, but it often encounters issues with stability and efficiency. Non-learning registration approaches rely on optimizing similarity metrics between fixed and moving images, which can be expensive in terms of time and space complexity. This problem can be exacerbated when the images are large or there are significant deformations between them. Recently, deep learning, specifically convolutional neural network (CNN)-based methods, have been explored as an effective solution to the weaknesses of non-learning approaches. To further advance learning approaches in image registration, we introduce an attention mechanism in the deformable image registration problem. Our proposed approach is based on a Transformer framework called AiR, which can be efficiently trained on GPGPU devices. We treat the image registration problem as a language translation task and use the Transformer to learn the deformation field. The method learns an unsupervised generated deformation map and is tested on two benchmark datasets. In summary, our approach shows promising effectiveness in addressing stability and efficiency issues in image registration tasks. The source code of AiR is available on Github.