Name
Abstract | ||
|---|---|---|
In this paper, we propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation, i.e., translating between two domains which have no paired training data available but each have paired training data with a third domain. We employ a single generator which has an encoder-decoder structure and analyze different implementations of domain conditional normalization to obtain the desired target domain output. The validation benchmark uses RGB-depth pairs and RGB-semantic pairs for training and compares performance for the depth-semantic translation task. The proposed approaches improve in qualitative and quantitative terms over the compared methods, while using much fewer parameters. Code available at: https://github.com/samarthshukla/dcn |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/WACV48630.2021.00355 | 2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021 |
DocType | ISSN | Citations |
Conference | 2472-6737 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samarth Shukla | 1 | 0 | 0.68 |
| Andrés Romero | 2 | 9 | 3.33 |
| Luc Van Gool | 3 | 27566 | 1819.51 |
| Radu Timofte | 4 | 1880 | 118.45 |