AdvEnt: Adversarial Entropy minimization for domain adaptation in semantic segmentation

Tuan-Hung Vu   Himalaya Jain   Maxime Bucher   Matthieu Cord   Patrick Pérez

CVPR 2019

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) 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.

Video

BibTeX

@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}
}