ForkGAN: Seeing into the Rainy Night
Abstract
We present a ForkGAN for task-agnostic image translation that can boost multiple vision tasks in adverse weather conditions. Three tasks of image localization/retrieval, semantic image segmentation, and object detection are evaluated. The key challenge is achieving high-quality image translation without any explicit supervision, or task awareness. Our innovation is a fork-shape generator with one encoder and two decoders that disentangles the domain-specific and domain-invariant information. We force the cyclic translation between the weather conditions to go through a common encoding space, and make sure the encoding features reveal no information about the domains. Experimental results show our algorithm produces state-of-the-art image synthesis results and boost three vision tasks' performances in adverse weathers.
Cite
Text
Zheng et al. "ForkGAN: Seeing into the Rainy Night." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58580-8_10Markdown
[Zheng et al. "ForkGAN: Seeing into the Rainy Night." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/zheng2020eccv-forkgan/) doi:10.1007/978-3-030-58580-8_10BibTeX
@inproceedings{zheng2020eccv-forkgan,
title = {{ForkGAN: Seeing into the Rainy Night}},
author = {Zheng, Ziqiang and Wu, Yang and Han, Xinran and Shi, Jianbo},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58580-8_10},
url = {https://mlanthology.org/eccv/2020/zheng2020eccv-forkgan/}
}