Deep Object Co-Segmentation (DOCS) - PyTorch

This is the Pytorch implementation of our paper Deep Object Co-Segmentation published at ACCV18. For more information, you can visit the project page. You can also find our caffe version here.

Requirements

• Python 3
• Pytorch >= 1.0
• gcc (tested with gcc 8)

Tested on Ubuntu 20.04 with pytorch 1.5, CUDA 10.2 and gcc 8.0 .

NOTE: for using it with older version of pytorch (0.4.1) please check v1.0 .

Installation

git clone https://github.com/ohosseini/DOCS-pytorch.git
cd DOCS-pytorch
bash install.sh

Demo

First download the model from here and put it in DOCS-pytorch directory.

Then you can run the demo with

bash demo.sh

For more information on how to apply the demo on other images you can check

python demo.py --help

Comparision with Caffe version

The numbers are compareable to the original ones in the paper which are generated using our main code (DOCS-caffe).

		DOCS-caffe	DOCS-pytorch
MSRC	P	95.4	92.0
	J	82.9	82.3
Internet	P	93.5	92.9
	J	72.6	72.0
iCoseg	P	95.1	94.1
	J	84.2	84.0

Citation

If you use this code, please cite our publication:

Deep Object Co-Segmentation, Weihao Li*, Omid Hosseini Jafari*, Carsten Rother, ACCV 2018.

@InProceedings{DOCS_ACCV18,
  title={Deep Object Co-Segmentation},
  author={Li, Weihao and Hosseini Jafari, Omid and Rother, Carsten},
  booktitle={ACCV},
  year={2018}
}

Acknowledgments

We used the Pytorch implementation of correlation layer from "Pytorch implementation of FlowNet 2.0" https://github.com/NVIDIA/flownet2-pytorch.