Self-supervised Video Object Segmentation by Motion Grouping

This code accompanies the paper: Self-supervised Video Object Segmentation by Motion Grouping

Charig Yang, Hala Lamdouar, Erika Lu, Andrew Zisserman, Weidi Xie.

ICCV 2021

Project page: https://charigyang.github.io/motiongroup/

Requirements :

pytorch (tested on 1.7, although any recent version should work)
cvbase
einops
tensorboardX

Datasets :

• DAVIS 2016 can be used as-is.
• The rest has to be converted to DAVIS format. Some helper functions are available in tools.
• MoCA needs to be processed. See Supplementary Material for the paper for details. Helper functions are available in tools. The (already filtered) dataset is also available on google drive: https://drive.google.com/drive/u/2/folders/1x-owzr9Voz65NQghrN_H1LEYDaaQP5n1, which can be used as-is after download.
• Precomputed flows can be generated from raft/run_inference.py

Training :

python train.py --dataset DAVIS --flow_to_rgb

Inference :

python eval.py --dataset DAVIS  --flow_to_rgb --inference --resume_path {}

Benchmark :

• For DAVIS: use the official evaluation code: https://github.com/fperazzi/davis
• For MOCA: use tools/MoCA_eval.py

How to use this on your own data :

• Generate optical flow from your dataset using raft/inference.py
• Edit setup_dataset in config.py to include your dataset, and add this to the choices in parser.add_argument('--dataset') in train.py and eval.py
• Follow the training and inference instructions above with your own --dataset argument. Use --resume_path {} if you are fine-tuning.

Issues/questions/pull requests :

are very welcome.

Reference :

If you find this helpful in your research, we would be grateful if you cite our work

@InProceedings{yang2021selfsupervised,
  title={Self-supervised Video Object Segmentation by Motion Grouping}, 
  author={Charig Yang and Hala Lamdouar and Erika Lu and Andrew Zisserman and Weidi Xie},
  booktitle={ICCV},
  year={2021},
}