Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation
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The difficulty of annotating training data is a major obstacle to using CNNs for low-level tasks in video. Synthetic data often does not generalize to real videos, while unsupervised methods require heuristic n losses. Proxy tasks can overcome these issues, and start by training a network for a task for which annotation is easier or which can be trained unsupervised. The trained network is then fine-tuned for the original task using small amounts of ground truth data. Here, we investigate frame interpolation as a proxy task for optical flow. Using real movies, we train a CNN unsupervised for temporal interpolation. Such a network implicitly estimates motion, but cannot handle untextured regions. By fi ne-tuning on small amounts of ground truth flow, the network can learn to fill in homogeneous regions and compute full optical flow fi elds. Using this unsupervised pre-training, our network outperforms similar architectures that were trained supervised using synthetic optical flow.
| Author(s): | Jonas Wulff and Michael J. Black |
| Links: | |
| Book Title: | German Conference on Pattern Recognition (GCPR) |
| Volume: | LNCS 11269 |
| Pages: | 567--582 |
| Year: | 2018 |
| Month: | October |
| Publisher: | Springer, Cham |
| Project(s): | |
| BibTeX Type: | Conference Paper (inproceedings) |
| DOI: | https://doi.org/10.1007/978-3-030-12939-2_39 |
| Electronic Archiving: | grant_archive |
BibTeX
@inproceedings{Wulff:GCPR:2018,
title = {Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation},
booktitle = {German Conference on Pattern Recognition (GCPR)},
abstract = {The difficulty of annotating training data is a major obstacle to using CNNs for low-level tasks in video. Synthetic data often does not generalize to real videos, while unsupervised methods require heuristic n losses. Proxy tasks can overcome these issues, and start by training a network for a task for which annotation is easier or which can be trained unsupervised. The trained network is then fine-tuned for the original task using small amounts of ground truth data. Here, we investigate frame interpolation
as a proxy task for optical flow. Using real movies, we train a CNN unsupervised for temporal interpolation. Such a network implicitly estimates motion, but cannot handle untextured regions. By fi ne-tuning on small amounts of ground truth flow, the network can learn to fill in homogeneous regions and compute full optical flow fi elds. Using this unsupervised pre-training, our network outperforms similar architectures that were trained supervised using synthetic optical flow.},
volume = {LNCS 11269},
pages = {567--582},
publisher = {Springer, Cham},
month = oct,
year = {2018},
author = {Wulff, Jonas and Black, Michael J.},
doi = {https://doi.org/10.1007/978-3-030-12939-2_39},
month_numeric = {10}
}