ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Abstract
Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks, generalizing well to diverse testing environments remains a major challenge. In numerous real-world applications, there is indeed a large gap between data distributions in train and test domains, which results in severe performance loss at run-time. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. To this end, we propose two novel, complementary methods using (i) entropy loss and (ii) adversarial loss respectively. We demonstrate state-of-the-art performance in semantic segmentation on two challenging "synthetic-2-real" set-ups and show that the approach can also be used for detection.
Cite
Text
Vu et al. "ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00262Markdown
[Vu et al. "ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/vu2019cvpr-advent/) doi:10.1109/CVPR.2019.00262BibTeX
@inproceedings{vu2019cvpr-advent,
title = {{ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation}},
author = {Vu, Tuan-Hung and Jain, Himalaya and Bucher, Maxime and Cord, Matthieu and Perez, Patrick},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2019},
doi = {10.1109/CVPR.2019.00262},
url = {https://mlanthology.org/cvpr/2019/vu2019cvpr-advent/}
}