update readme

README.md

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 ![](figures/teaser.jpg)
 
 [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://arxiv.org/abs/1811.12833)  
- [Tuan-Hung Vu](https://tuanhungvu.github.io/),  [Himalaya Jain](), [Maxime Bucher](https://maximebucher.github.io/), [Matthieu Cord](http://webia.lip6.fr/~cord/), [Patrick Pérez](https://ptrckprz.github.io/)  
+ [Tuan-Hung Vu](https://tuanhungvu.github.io/),  [Himalaya Jain](https://scholar.google.fr/citations?user=Xl7SNlsAAAAJ), [Maxime Bucher](https://maximebucher.github.io/), [Matthieu Cord](http://webia.lip6.fr/~cord/), [Patrick Pérez](https://ptrckprz.github.io/)  
  valeo.ai, France  
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (**Oral**)
 
@@ -20,5 +20,8 @@ If you find this code useful for your research, please cite our [paper](https://
 }
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+## 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.
+
 ## Demo
 [![](http://img.youtube.com/vi/Ihmz0yEqrq0/0.jpg)](http://www.youtube.com/watch?v=Ihmz0yEqrq0 "")