# Show, Attend and Translate: Unpaired Multi-Domain Image-to-Image Translation with Visual Attention

arXiv: https://arxiv.org/abs/1811.07483 · HTML: https://mlsanity.com/paper/1811.07483/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Multimarginal generative modeling with stochastic interpolants](https://mlsanity.com/paper/2310.03695/) (2023)
- [Bridging Human Concepts and Computer Vision for Explainable Face Verification](https://mlsanity.com/paper/2403.08789/) (2024)
- [A degree of image identification at sub-human scales could be possible with more advanced clusters](https://mlsanity.com/paper/2308.05092/) (2023)

## Abstract (arXiv)

Recently unpaired multi-domain image-to-image translation has attracted great interests and obtained remarkable progress, where a label vector is utilized to indicate multi-domain information. In this paper, we propose SAT (Show, Attend and Translate), an unified and explainable generative adversarial network equipped with visual attention that can perform unpaired image-to-image translation for multiple domains. By introducing an action vector, we treat the original translation tasks as problems of arithmetic addition and subtraction. Visual attention is applied to guarantee that only the regions relevant to the target domains are translated. Extensive experiments on a facial attribute dataset demonstrate the superiority of our approach and the generated attention masks better explain what SAT attends when translating images.