Joint-task Self-supervised Learning

for Temporal Correspondence

1Univerisity of California, Merced 2Nvidia 3Carnegie Mellon University

Neural Information Processing Systems (NeurIPS), 2019


This paper proposes to learn reliable dense correspondence from videos in a self-supervised manner. Our learning process integrates two highly related tasks: tracking large image regions and establishing fine-grained pixel-level associations between consecutive video frames. We exploit the synergy between both tasks through a shared inter-frame affinity matrix, which simultaneously models transitions between video frames at both the region- and pixel-levels. While region-level localization helps reduce ambiguities in fine-grained matching by narrowing down search regions; fine-grained matching provides bottom-up features to facilitate region-level localization. Our method outperforms the state-of-the-art self-supervised methods on a variety of visual correspondence tasks, including video-object and part-segmentation propagation, keypoint tracking, and object tracking. Our self-supervised method even surpasses the fully-supervised affinity feature representation obtained from a ResNet-18 pre-trained on the ImageNet.


Paper & Code

Pytorch Code: [link]

Paper: [link]


    author = {Li, Xueting and Liu, Sifei and De Mellow, Shalini and Wang, Xiaolong and Kautz, Jan and Yang, Ming-Hsuan},
    title = {Joint-task Self-supervised Learning for Temporal Correspondence},
    booktitle = {NeurIPS},
    year = {2019}

Poster [link]