Self-Supervised AutoFlow
Hsin-Ping Huang, Charles Herrmann, Junhwa Hur, Erika Lu, Kyle Sargent, Austin Stone, Ming-Hsuan Yang, Deqing Sun
Abstract
Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric. Observing a strong correlation between the ground truth search metric and self-supervised losses, we introduce self-supervised AutoFlow to handle real-world videos without ground truth labels. Using self-supervised loss as the search metric, our self-supervised AutoFlow performs on par with AutoFlow on Sintel and KITTI where ground truth is available, and performs better on the real-world DAVIS dataset. We further explore using self-supervised AutoFlow in the (semi-)supervised setting and obtain competitive results against the state of the art.
BibTeX
@inproceedings{cvpr2023_selfsupervisedau,
title = {Self-Supervised AutoFlow},
author = {Hsin-Ping Huang and Charles Herrmann and Junhwa Hur and Erika Lu and Kyle Sargent and Austin Stone and Ming-Hsuan Yang and Deqing Sun},
booktitle = {CVPR 2023},
year = {2023}
}