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Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation


Conference Paper


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
Book Title: German Conference on Pattern Recognition (GCPR)
Year: 2018
Month: October

Department(s): Perceiving Systems
Research Project(s): Learning Optical Flow
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Links: pdf


  title = {Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation},
  author = {Wulff, Jonas and Black, Michael J.},
  booktitle = {German Conference on Pattern Recognition (GCPR)},
  month = oct,
  year = {2018},
  month_numeric = {10}