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# A Two-Stream Conditional Generative Adversarial Network for Improving Semantic Predictions in Urban Driving Scenes

![Overview of the proposed post-processing framework:](Dataset_prep.png)

The proposed scheme is a Two-Stream Conditional Generative Adversarial Network (TScGAN), with one stream having initial semantic segmentation masks predicted by an existing CNN while the other stream utilizes scene images to retain high-level information under a supervised residual network structure. In addition, TScGAN incorporates a novel dynamic weighting mechanism, which leads to significant and consistent gain in segmentation performance.

![Architecture of the TScGAN](Dataset_prep.png)
Please refer to our [paper]() for more details.

# Download

Click [here]() to download the code. 

The pretrained models are found in [TScGAN]()

# Results - TScGAN

Several comparative tests on public benchmark driving databases, including Cityscapes, Mapillary, and Berkeley DeepDrive100K, demonstrate the effectiveness of the proposed method when used with state-of-the-art CNN-based semantic segmentation models.

![Comparison of results with state of-the-art algorithms on the Cityscapes validation dataset.](Dataset_prep.png)

# Citing TScGAN
If you find this framework useful in your work, please cite the paper: 


# Contributions
If you find any issue running the code, you can report it in the issues section.

# Acknowledgements
University Technology Belfort-Montbrliard, France [UTBM](https://www.utbm.fr/)
Connaissance et Intelligence Artificielle Distribuées [CIAD](http://www.ciad-lab.fr/)

# Inspiration
We hope that this will benefit the community and researchers working in the field of autonomous driving.