Awesome

CF-Caffe

Caffe designed for Deep Context Features

Basic Citation

If you use CF-Caffe, please cite:

@inproceedings{hu2018direction,
  title={Direction-aware spatial context features for shadow detection},
  author={Hu, Xiaowei and Zhu, Lei and Fu, Chi-Wing and Qin, Jing and Heng, Pheng-Ann},
  booktitle={IEEE conference on computer vision and pattern recognition (CVPR)},
  pages={7454--7462},
  year={2018}
}

@article{hu2020direction,
  title={Direction-aware spatial context features for shadow detection and removal},
  author={Hu, Xiaowei and Fu, Chi-Wing and Zhu, Lei and Qin, Jing and Heng, Pheng-Ann},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={42},
  number={11},
  pages={2795--2808},
  year={2020}
}

@inproceedings{jia2014caffe,
  title={Caffe: Convolutional architecture for fast feature embedding},
  author={Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  booktitle={ACM international conference on Multimedia},
  pages={675--678},
  year={2014}
}

Installation

1. Clone this repository.

   git clone https://github.com/xw-hu/CF-Caffe.git
   

2. Build CF-Caffe

   *This model is tested on Ubuntu 16.04, CUDA 8.0.

   Follow the Caffe installation instructions here: http://caffe.berkeleyvision.org/installation.html

   make all -jXX
   

3. If you want to use MATLAB or Python:

   make matcaffe
   make pycaffe
   

Models

If you use these models, please cite their papers accordingly.

1. Segmentation models in examples/segmentation/:

   Deeplab v1, Deeplab v3, Deeplab v3 plus, PSPNet, PSANet, Non-local Network (FPN based).

2. This version of Caffe is used in:

DAF-Net

@InProceedings{Hu_2019_CVPR,
     author = {Hu, Xiaowei and Fu, Chi-Wing and Zhu, Lei and Heng, Pheng-Ann},
     title = {Depth-Attentional Features for Single-Image Rain Removal},
     booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
     pages={8022--8031},
     year = {2019}
}

DSC

@InProceedings{Hu_2018_CVPR,
     author = {Hu, Xiaowei and Zhu, Lei and Fu, Chi-Wing and Qin, Jing and Heng, Pheng-Ann},
     title = {Direction-Aware Spatial Context Features for Shadow Detection},
     booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
     pages={7454--7462},
     year = {2018}
}

@article{hu2019direction,
     author = {Hu, Xiaowei and Fu, Chi-Wing and Zhu, Lei and Qin, Jing and Heng, Pheng-Ann},
     title = {Direction-Aware Spatial Context Features for Shadow Detection and Removal},
     journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
     year = {2019},
     note={to appear}
}

SAC-Net

@article{hu2020sac,
     author = {Hu, Xiaowei and Fu, Chi-Wing and Zhu, Lei and Wang, Tianyu and Heng, Pheng-Ann},
     title = {SAC-Net: Spatial Attenuation Context for Salient Object Detection},
     journal = {IEEE Transactions on Circuits and Systems for Video Technology},
     year = {2020},
     note = {to appear},
}

GNLB

@article{zhu2020saliency,
     author = {Zhu, Lei and Hu, Xiaowei and Fu, Chi-Wing and Qin, Jing and Heng, Pheng-Ann},
     title = {Saliency-Aware Texture Smoothing},
     journal={IEEE Transactions on Visualization and Computer Graphics},
     volume={26},
     number={7},
     pages={2471-2484},
     year={2020} }

RADF

@inproceedings{hu18recurrently,
     author = {Hu, Xiaowei and Zhu, Lei and Qin, Jing and Fu, Chi-Wing and Heng, Pheng-Ann},
     title = {Recurrently Aggregating Deep Features for Salient Object Detection},
     booktitle = {AAAI},
     pages={6943--6950},
     year = {2018}
}