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https://github.com/Ha0Tang/SelectionGAN

[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
https://github.com/Ha0Tang/SelectionGAN

adversarial-learning computer-graphics computer-vision cross-view cvpr-2019 cvpr19 cvpr2019 cvusa-dataset dayton deep-learning gans generative-adversarial-network image-generation image-manipulation image-to-image-translation image-translation pytorch semantic-maps

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[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

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# SelectionGAN for Guided Image-to-Image Translation

### [CVPR Paper](https://arxiv.org/abs/1904.06807) | [TPAMI Paper](https://arxiv.org/abs/2002.01048) | [Guided-I2I-Translation-Papers](https://github.com/Ha0Tang/Guided-I2I-Translation-Papers)



![SelectionGAN Results](./imgs/motivation.jpg)



## Citation

If you use this code for your research, please cite our papers.

```

@article{tang2022multi,

  title={Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation},

  author={Tang, Hao and Torr, Philip HS and Sebe, Nicu},

  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},

  year={2022}

}



@inproceedings{tang2019multi,

  title={Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation},

  author={Tang, Hao and Xu, Dan and Sebe, Nicu and Wang, Yanzhi and Corso, Jason J. and Yan, Yan},

  booktitle={CVPR},

  year={2019}

}



@article{tang2023edge,

  title={Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis},

  author={Tang, Hao and Qi, Xiaojuan and Sun, Guolei, and Xu, Dan and and Sebe, Nicu and Timofte, Radu and Van Gool, Luc},

  journal={ICLR},

  year={2023}

}



@article{tang2022local,

  title={Local and Global GANs with Semantic-Aware Upsampling for Image Generation},

  author={Tang, Hao and Shao, Ling and Torr, Philip HS and Sebe, Nicu},

  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},

  year={2022}

}



@inproceedings{tang2020local,

  title={Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation},

  author={Tang, Hao and Xu, Dan and Yan, Yan and Torr, Philip HS and Sebe, Nicu},

  booktitle={CVPR},

  year={2020}

}



@article{wu2022cross,

  title={Cross-view panorama image synthesis with progressive attention GANs},

  author={Wu, Songsong and Tang, Hao and Jing, Xiao-Yuan and Qian, Jianjun and Sebe, Nicu and Yan, Yan and Zhang, Qinghua},

  journal={Elsevier Pattern Recognition (PR)},

  year={2022}

}



@article{wu2022cross,

  title={Cross-View Panorama Image Synthesis},

  author={Wu, Songsong and Tang, Hao and Jing, Xiao-Yuan and Zhao, Haifeng and Qian, Jianjun and Sebe, Nicu and Yan, Yan},

  journal={IEEE Transactions on Multimedia (TMM)},

  year={2022}

}



@inproceedings{ren2021cascaded,

  title={Cascaded Cross MLP-Mixer GANs for Cross-View Image Translation},

  author={Ren, Bin and Tang, Hao and Sebe, Nicu},

  booktitle={BMVC},

  year={2021}

}

```



In the meantime, check out our related papers:

- cross-view image translation: 

  - [Cross-View Panorama Image Synthesis (TMM 2022)](https://github.com/sswuai/PanoGAN)

  - [Cascaded Cross MLP-Mixer GANs for Cross-View Image Translation (BMVC 2021 Oral)](https://github.com/Amazingren/CrossMLP)

- person image generation: 

  - [XingGAN for Person Image Generation (ECCV 2020)](https://github.com/Ha0Tang/XingGAN)

  - [Bipartite Graph Reasoning GANs for Person Image Generation (BMVC 2020 Oral)](https://github.com/Ha0Tang/BiGraphGAN)

- semantic image synthesis: 

  - [Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis (ICLR 2023)](https://github.com/Ha0Tang/ECGAN)

  - [Dual Attention GANs for Semantic Image Synthesis (ACM MM 2020)](https://github.com/Ha0Tang/DAGAN)

  - [Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation (CVPR 2020)](https://github.com/Ha0Tang/LGGAN)



More related guided image-to-image translation papers can be found in [this page](https://github.com/Ha0Tang/Guided-I2I-Translation-Papers).



## To Do List

- [x] SelectionGAN: CVPR version

- [x] SelectionGAN++: TPAMI version

- [x] Pix2pix++: Takes RGB image and target semantic map as inputs: [code](./cross_view_v2)

- [x] X-ForK++: Takes RGB image and target semantic map as inputs: [code](./cross_view_v2)

- [x] X-Seq++: Takes RGB image and target semantic map as inputs: [code](./cross_view_v2)



## Others

- [How to write a great science paper](https://www.nature.com/articles/d41586-019-02918-5)



## Acknowledgments

This source code is inspired by [Pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).



## Contributions

If you have any questions/comments/bug reports, feel free to open a github issue or pull a request or e-mail to the author Hao Tang ([[email protected]]([email protected])).



## Collaborations

I'm always interested in meeting new people and hearing about potential collaborations. If you'd like to work together or get in contact with me, please email [email protected]. Some of our projects are listed [here](https://github.com/Ha0Tang).

___

*In life, patience is the key. It's much better to be going somewhere slowly than nowhere fast.*