Unsupervised Attention-guided Image-to-Image Translation

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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


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 which are jointly adversarially trained with the generators and discriminators. We empirically demonstrate that our approach is able to attend to relevant regions in the image without requiring any additional supervision, and that by doing so it achieves more realistic mappings compared to recent approaches.