Awesome
MuG
code for CVPR 2020 paper: Learning Video Object Segmentation from Unlabeled Videos
Pre-compute results
The segmentation results of object--level zero-shot VOS (DAVIS16-val dataset), instance-level zero-shot VOS (DAVIS2017-test-dev dataset) and one-shot VOS (DAVIS2016-val and DAVIS 2017-val datasets) under both unsupervised and weakly supervised conditionscan be download from GoogleDrive.
Code runing
- Setup environment: Pytorch 1.1.0, tqdm, scipy 1.2.1.
- Prepare training data. Download training datasets from Got10k tracking dataset or Youtube-VOS dataset. Generate a csv file in a format of 'GOT-10k_Train_000001, 120'. The first term is video name, the second term is video length.
- Download the weakly supervised saliency generation model and inference code from here and unsupervised saliency detection from [here] (https://github.com/ruanxiang/mr_saliency)
- Change all the paths in MuG_GOT_global_new_residual.py, my_model_new_residual.py and libs/model_match_residual.py. Run run_train_all_GOT_global_new_residual.sh for network training.
- Run run_ZVOS.sh for network inference.
Other related projects/papers:
Zero-shot Video Object Segmentation via Attentive Graph Neural Networks
Saliency-Aware Geodesic Video Object Segmentation (CVPR15)
Learning Unsupervised Video Primary Object Segmentation through Visual Attention (CVPR19)
Joint-task Self-supervised Learning for Temporal Correspondence
Any comments, please email: carrierlxk@gmail.com