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Unsupervised Attention-guided Image-to-Image Translation
Youssef Alami Mejjati · Christian Richardt · James Tompkin · Darren Cosker · Kwang In Kim

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 210 #31

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.

Author Information

Youssef Alami Mejjati (University of Bath)
Christian Richardt (University of Bath)
James Tompkin (Brown University)
Darren Cosker (University of Bath)
Kwang In Kim (University of Bath)

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