Unsupervised Attention-guided Image-to-Image Translation

Youssef Alami Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

212 Citations (SciVal)


Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene. Motivated by the important role of attention in human perception, we tackle this limitation by introducing unsupervised attention mechanisms that are jointly adversarially trained with the generators and discriminators. We demonstrate qualitatively and quantitatively that our approach attends to relevant regions in the image without requiring supervision, which creates more realistic mappings when compared to those of recent approaches.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 665992
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 31 (NIPS), 2018
EditorsS. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett
PublisherNeural Information Processing Systems Foundation, Inc.
Number of pages22
Publication statusPublished - 31 Dec 2018
EventNIPS 2018 - 32nd Conference on Neural Information Processing Systems -
Duration: 3 Dec 20188 Dec 2018

Publication series

NameNIPS Proceedings
PublisherNeural Information Processing Systems Foundation, Inc.
ISSN (Electronic)1049-5258


ConferenceNIPS 2018 - 32nd Conference on Neural Information Processing Systems


Dive into the research topics of 'Unsupervised Attention-guided Image-to-Image Translation'. Together they form a unique fingerprint.

Cite this