Zero-Pair Image to Image Translation Using Domain Conditional Normalization

Samarth Shukla, Andres Romero, Luc Van Gool, Radu Timofte; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 3512-3519

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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Shukla_2021_WACV, author = {Shukla, Samarth and Romero, Andres and Van Gool, Luc and Timofte, Radu}, title = {Zero-Pair Image to Image Translation Using Domain Conditional Normalization}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2021}, pages = {3512-3519} }