This is the implementation of Qingxing Cao et al.'s CVPR-17 work Attention-Aware Face Hallucination via Deep Reinforcement Learning.
Please cite Attention-FH in your publications if it helps your research:
@article{Cao2017Attention,
title={Attention-Aware Face Hallucination via Deep Reinforcement Learning},
author={Cao, Qingxing and Lin, Liang and Shi, Yukai and Liang, Xiaodan and Li, Guanbin},
pages={1656-1664},
year={2017},
}
- Computer with Linux or OSX
- Torch-7
- For training, an NVIDIA GPU is strongly recommended for speed. CPU is supported but training is very slow.
- Cuda 8.0
- Install Torch: http://torch.ch/docs/getting-started.html
- Install Element-Research/rnn: https://github.com/Element-Research/rnn
- After all dependencies installed, you should put your own training and testing files as:
../lfw_funneled_dev_128/train/Aaron_Eckhart/Aaron_Eckhart_0001.jpg
../lfw_funneled_dev_128/test/Aaron_Guiel/Aaron_Guiel_0001.jpg
Our data has processed with 2 points aligned and crop the centric part. We suggest you to use CFSS or other face alignment methods to pre-process your data. Then you can train the model by using the following command:
python RNN_main.lua
If you have any questions or suggestions of this work, please feel free to contact the authors by sending email to caoqxATmail2.sysu.edu.cn or shiyk3ATmail2.sysu.edu.cn.
This work was benifited a lot from DCGAN and Recurrent Models of Visual Attention, thank for their great job.