COSNet

Code for CVPR 2019 paper:

See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks

Xiankai Lu, Wenguan Wang, Chao Ma, Jianbing Shen, Ling Shao, Fatih Porikli

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###The pre-trained model and testing code:

Quick Start

1. Install pytorch (version:1.0.1).

2. Download the pretrained model. Run 'test_coattention_conf.py' and change the davis dataset path, pretrainde model path and result path.

3. Run command: python test_coattention_conf.py --dataset davis --gpus 0

4. Post CRF processing code: https://github.com/lucasb-eyer/pydensecrf

The pretrained weight can be download from GoogleDrive or BaiduPan, pass code: xwup.

The segmentation results on DAVIS, FBMS and Youtube-objects can be download from GoogleDrive or BaiduPan, pass code: nzbq.

Citation

If you find the code and dataset useful in your research, please consider citing:

@InProceedings{Lu_2019_CVPR,
author = {Lu, Xiankai and Wang, Wenguan and Ma, Chao and Shen, Jianbing and Shao, Ling and Porikli, Fatih},
title = {See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019}
}

Other related projects/papers:

Saliency-Aware Geodesic Video Object Segmentation (CVPR15)

Learning Unsupervised Video Primary Object Segmentation through Visual Attention (CVPR19)

Any comments, please email: [email protected]