Code will be available soon.
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, Patrick Pérez
valeo.ai, France
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Oral)
If you find this code useful for your research, please cite our paper:
@inproceedings{vu2018advent,
title={ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation},
author={Vu, Tuan-Hung and Jain, Himalaya and Bucher, Maxime and Cord, Mathieu and P{\'e}rez, Patrick},
booktitle={CVPR},
year={2019}
}
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) an entropy loss and (ii) an 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.
