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Joint-task Self-supervised Learning for Temporal Correspondence

Project | Paper

Overview

Joint-task Self-supervised Learning for Temporal Correspondence

Xueting Li*, Sifei Liu*, Shalini De Mello, Xiaolong Wang, Jan Kautz, Ming-Hsuan Yang.

(* equal contributions)

In Neural Information Processing Systems (NeurIPS), 2019.

Citation

If you use our code in your research, please use the following BibTex:

@inproceedings{uvc_2019,
    Author = {Xueting Li and Sifei Liu and Shalini De Mello and Xiaolong Wang and Jan Kautz and Ming-Hsuan Yang},
    Title = {Joint-task Self-supervised Learning for Temporal Correspondence},
    Booktitle = {NeurIPS},
    Year = {2019},
}

Instance segmentation propagation on DAVIS2017

Method J_mean J_recall J_decay F_mean F_recall F_decay
Ours 0.563 0.650 0.289 0.592 0.641 0.354
Ours - track 0.577 0.683 0.263 0.613 0.698 0.324

Prerequisites

The code is tested in the following environment:

  • Ubuntu 16.04
  • Pytorch 1.1.0, tqdm, scipy 1.2.1

Testing on DAVIS2017

Testing without tracking

To test on DAVIS2017 for instance segmentation mask propagation, please run:

python test.py -d /workspace/DAVIS/ -s 480

Important parameters:

  • -c: checkpoint path.
  • -o: results path.
  • -d: DAVIS 2017 dataset path.
  • -s: test resolution, all results in the paper are tested on 480p images, i.e. -s 480.

Please check the test.py file for other parameters.

Testing with tracking

To test on DAVIS2017 by tracking & propagation, please run:

python test_with_track.py -d /workspace/DAVIS/ -s 480

Similar parameters as test.py, please see the test_with_track.py for details.

Testing on the VIP dataset

To test on VIP, please run the following command with your own VIP path:

python test_mask_vip.py -o results/VIP/category/ --scale_size 560 560 --pre_num 1 -d /DATA/VIP/VIP_Fine/Images/ --val_txt /DATA/VIP/VIP_Fine/lists/val_videos.txt -c weights/checkpoint_latest.pth.tar

and then:

python eval_vip.py -g DATA/VIP/VIP_Fine/Annotations/Category_ids/ -p results/VIP/category/

Testing on the JHMDB dataset

Please check out this branch. The code is borrowed from TimeCycle.

Training on Kinetics

Dataset

We use the kinetics dataset for training.

Training command

python track_match_v1.py --wepoch 10 --nepoch 30 -c match_track_switch --batchsize 40 --coord_switch 0 --lc 0.3

Acknowledgements