Unsupervised Attention-guided Image-to-Image Translation
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.
To conduct image-to-image translation, this paper introduces attention mechanisms to CycleGAN [1]. The divergence between only the relevant parts of both source and target domains is minimized. Compared to recent approaches, the proposed method performs better in conserving the background and gets the lowest FID in quantitative analysis.   Related works Related works follows a clear structure. For image-to-image translation, it introduces supervised and unsupervised methods, followed by mentioning the problem which is the background is affected, and then it refers to some related research on attention learning.  Approach The idea of having continuous attention map for generator is good, and it is also good to read the authors’ explanations of why and how it is valuable. The reason why they use segmented attention map for discriminator is also well presented.  Experiemnts It is interesting to see Figure 3 and Figure 5 have translation results when there is no interested class in source domain, showing this approach applies appropriate attention mechanisms. Some experiment details like the choices of hyperparameters are absent, not being presented in the submission paper or supplementary material. But in the end of the paper, the authors say the code will be released, hopefully this will be the case.  Lack of qualitative analysis about UNIT approach[4].  Other comments The readability of this paper is good. The structure and diagram of the proposed attention method are clearly presented. In conclusion, the authors mention one constraint of the proposed method, which is not being robust to shape changes, but this paper doesn’t involve related examples. It would be better to have the mentioned horse-to-bird results in supplementary material. L210: the last row of Figure 5 doesn’t seem to be an albino tiger, but the last third row is, which the last row of the top part.