GraphMemVOS

Code for ECCV 2020 spotlight paper: Video Object Segmentation with Episodic Graph Memory Networks

Testing

1. Install python (3.6.5), pytorch (version:1.0.1) and requirements in the requirements.txt files. Download the DAVIS-2017 dataset.

2. Download the pretrained model from googledrive and put it into the workspace_STM_alpha files.

3. Run 'run_graph_memory_test.sh' and change the davis dataset path, pretrainde model path and result path and the paths in local_config.py.

The segmentation results can be download from googledrive.

Results

1. DAVIS ( Val 2017):

In the inference stage, we ran using the default size of DAVIS (480p).

2. YouTube-VOS (Val 2018):

3. DAVIS-2016:

4. Youtube-Objects:

Citation

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

@inproceedings{lu2020video,  
 title={Video Object Segmentation with Episodic Graph Memory Networks},  
 author={Lu, Xiankai and Wang, Wenguan and Martin, Danelljan and Zhou, Tianfei and Shen, Jianbing and Luc, Van Gool},  
 booktitle={ECCV},  
 year={2020}  
}

Other related projects/papers:

1. Zero-shot Video Object Segmentation via Attentive Graph Neural Networks, ICCV 2019 (https://github.com/carrierlxk/AGNN)

Acknowledge

1. Video object segmentation using space-time memory networks, ICCV 2019 (https://github.com/seoungwugoh/STM)
2. A Generative Appearance Model for End-to-End Video Object Segmentation, CVPR2019 (https://github.com/joakimjohnander/agame-vos)
3. https://github.com/lyxok1/STM-Training

Any comments, please email: [email protected]