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).

Mean J&F	J score	F score
82.8	80.0	85.2

2. YouTube-VOS (Val 2018):

J Seen	F Seen	J Unseen	F Unseen	Mean
80.7	85.1	74.0	80.9	80.2

3. DAVIS-2016:

J score	F score	Mean T
82.5	81.2	19.8

4. Youtube-Objects:

Airplane	Bird	Boat	Car	Cat	Cow	Dog	Horse	Motorbike	Train	Mean
86.1	75.7	68.6	82.4	65.9	70.5	77.1	72.2	63.8	47.8	71.4

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: carrierlxk@gmail.com