TimeCycle

Code for Learning Correspondence from the Cycle-consistency of Time (CVPR 2019, Oral). The code is developed based on the PyTorch framework. This repo includes the training code for learning semi-dense correspondence from unlabeled videos, and testing code for applying this correspondence on segmentation mask tracking in videos.

Citation

If you use our code in your research or wish to refer to the baseline results, please use the following BibTeX entry.

@inproceedings{CVPR2019_CycleTime,
    Author = {Xiaolong Wang and Allan Jabri and Alexei A. Efros},
    Title = {Learning Correspondence from the Cycle-Consistency of Time},
    Booktitle = {CVPR},
    Year = {2019},
}

Model and Result

Our trained model can be downloaded from here. The tracking performance on DAVIS-2017 for this model (without training on DAVIS-2017) is:

J_mean	J_recall	J_decay	F_mean	F_recall	F_decay
0.419	0.409	0.272	0.394	0.336	0.328

The training and testing procedures for this model are described as follows.

Dataset Preparation

Please read DATASET.md for downloading and preparing the VLOG dataset for training and DAVIS dataset for testing.

Training

Replace the input list in train_video_cycle_simple.py in the home folder as:

    params['filelist'] = 'YOUR_DATASET_FOLDER/vlog_frames_12fps.txt'

Then run the following code:

    python train_video_cycle_simple.py --checkpoint pytorch_checkpoints/release_model_simple

Testing

Replace the input list in test_davis.py in the home folder as:

    params['filelist'] = 'YOUR_DATASET_FOLDER/davis/DAVIS/vallist.txt'

Set up the dataset path YOUR_DATASET_FOLDER in run_test.sh . Then run the testing and evaluation code together:

    sh run_test.sh

Note that one can easily improve the results by increasing the "cropSize" in the script.

Acknowledgements

weakalign by Ignacio Rocco, Relja Arandjelović and Josef Sivic.

inflated_convnets_pytorch by Yana Hasson.

pytorch-classification by Wei Yang.