Multigrid Predictive Filter Flow for Unsupervised Learning on Videos

2 Apr 2019  ·  Shu Kong, Charless Fowlkes ·

We introduce multigrid Predictive Filter Flow (mgPFF), a framework for unsupervised learning on videos. The mgPFF takes as input a pair of frames and outputs per-pixel filters to warp one frame to the other. Compared to optical flow used for warping frames, mgPFF is more powerful in modeling sub-pixel movement and dealing with corruption (e.g., motion blur). We develop a multigrid coarse-to-fine modeling strategy that avoids the requirement of learning large filters to capture large displacement. This allows us to train an extremely compact model (4.6MB) which operates in a progressive way over multiple resolutions with shared weights. We train mgPFF on unsupervised, free-form videos and show that mgPFF is able to not only estimate long-range flow for frame reconstruction and detect video shot transitions, but also readily amendable for video object segmentation and pose tracking, where it substantially outperforms the published state-of-the-art without bells and whistles. Moreover, owing to mgPFF's nature of per-pixel filter prediction, we have the unique opportunity to visualize how each pixel is evolving during solving these tasks, thus gaining better interpretability.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Skeleton Based Action Recognition JHMDB Pose Tracking mgPFF+ft 1st PCK@0.1 58.4 # 1
PCK@0.2 78.1 # 1
PCK@0.3 85.9 # 1
PCK@0.4 89.8 # 1
PCK@0.5 92.4 # 1


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