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. Figure 1: By explicitly modeling attention, our algorithm is able to better alter the object of interest in unsupervised image-to-image translation tasks, without changing the background at the same time.
Youssef et al. REF propose unsupervised attention mechanisms that are jointly adversarially trained with the generators and discriminators to synthesize more realistic mappings.
Unsupervised Attention-guided Image to Image Translation
NIPS 2018
Computer Science