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arXiv · 1809.08317

Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation

Jonas Wulff·Michael J. Black
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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 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 fine-tuning on small amounts of ground truth flow, the network can learn to fill in homogeneous regions and compute full optical flow fields. Using this unsupervised pre-training, our network outperforms similar architectures that were trained supervised using synthetic optical flow.

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Jonas Wulff, Michael J. Black. 2018-09-21. Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation. https://arxiv.org/abs/1809.08317

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