Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2018 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) | human perception |
Field | DocType | Volume |
Image translation,Computer science,Artificial intelligence,Perception,Machine learning | Conference | 31 |
ISSN | Citations | PageRank |
1049-5258 | 7 | 0.43 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youssef A. Mejjati | 1 | 7 | 1.10 |
| Christian Richardt | 2 | 393 | 26.27 |
| James Tompkin | 3 | 331 | 25.38 |
| Darren Cosker | 4 | 285 | 29.09 |
| Kwang In Kim | 5 | 1625 | 78.90 |