Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 

Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning

This is a demo of our work 'Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning', presented at CVPR 2018. The work aims to learn a pixel-wise embedding, where similar pixels are close in the embedding space, for video object segmentation. More details can be found in the paper.

If you find it helpful for your research, please consider citing the paper:

@inproceedings{chen2018blazingly,
  title={Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning},
  author={Chen, Yuhua and Pont-Tuset, Jordi and Montes, Alberto and Van Gool, Luc},
  booktitle = {Computer Vision and Pattern Recognition (CVPR)},
  year={2018}
}

If you encounter any problem, feel free to contact me at yuhua[dot]chen[at]vision[dot]ee[dot]ethz[dot]ch.

Installation

  1. Clone the repo.
    git clone https://github.com/yuhuayc/fast-vos.git
    
  2. The implementation is based on the Caffe version from PSPNet. Clone the repo, and build Caffe and matcaffe (see: Caffe installation instructions)
    git clone https://github.com/hszhao/PSPNet.git
    make -j8 && make matcaffe
    
  3. Create a soft link of Caffe in the project folder.
    cd $VOS_ROOT/
    ln -s $CAFFE_ROOT caffe
    

Interactive Video Segmentation Demo

  1. An example script is provided in 'demo_interactive.m'.

  2. Pre-trained model can be downloaded at here, move it to $VOS_ROOT/models/interactive/

About

An implementation of our CVPR 2018 work 'Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning'

Resources

License

Releases

No releases published

Packages

No packages published

Languages