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Deep Region and Multi-label Learning for Facial Action Unit Detection (CVPR16)

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Intro
-----

This repository provides the codes for the CVPR16 paper, “[Deep Region and Multi-Label Learning for Facial Action Unit Detection](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhao_Deep_Region_and_CVPR_2016_paper.pdf)".
This code aims for training a convolutional network that contains a *region layer* for specializing the learned kernels on different facial regions, and meanwhile utilizes a multi-label cross-entropy to jointly learn 12 AUs.
This implementation is based on [Caffe Toolbox](https://github.com/BVLC/caffe).

File structure
--------------

Based on the caffe toolbox, we organize the source files as follows:

- `include/caffe/`: Header files that contains the declaration of our implemented layers

- `prototxt/`: Network architecture we used to compuare and report in our paper

- `src/caffe/layers/`: Source files of our implemented layers

- `box_layer.*`: Slice a 160x160 response map into an 8x8 uniform grid.
    
    - `image_data_layer_multilabel.cpp`: Load multiple labels for one image.
    
    - `multi_sigmoid_cross_entropy_loss_layer.*`: Multi-label loss.
    
    - `splice.*`: Concatenate 20 8x8 uniform grids to a 160x160 feature map.

More info
---------

- **Contact**:  Please send comments to Kaili Zhao (kailizhao@bupt.edu.cn)  
- **Citation**: If you use this code in your paper, please cite the following:
```
@inproceedings{zhao2016deep,
  title={Deep Region and Multi-Label Learning for Facial Action Unit Detection},
  author={Zhao, Kaili and Chu, Wen-Sheng and Zhang, Honggang},
  booktitle={CVPR},
  year={2016}
}
```

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