We introduce a self-supervised method for learning visual correspondence from unlabeled video. The main idea is to use cycle-consistency in time as free supervisory signal for learning visual representations from scratch. At training time, our model learns a feature map representation to be useful for performing cycle-consistent tracking. At test time, we use the acquired representation to find nearest neighbors across space and time. We demonstrate the generalizability of the representation -- without finetuning -- across a range of visual correspondence tasks, including video object segmentation, keypoint tracking, and optical flow. Our approach outperforms previous self-supervised methods and performs competitively with strongly supervised methods.
@article{arxiv.1903.07593,
title = {Learning Correspondence from the Cycle-Consistency of Time},
author = {Xiaolong Wang and Allan Jabri and Alexei A. Efros},
journal= {arXiv preprint arXiv:1903.07593},
year = {2019}
}