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About this paper
Ayush Shrivastava, Andrew Owens
The paper introduces Global Matching Random Walk (GMRW), a self-supervised method for the Tracking Any Point (TAP) problem that leverages a global matching transformer and a novel 'label warping' technique to prevent shortcut learning. GMRW achieves performance competitive with supervised methods on TapVid benchmarks without relying on labeled ground-truth data.
Key insights
- Self-supervised learning can achieve high spatial precision and long-range tracking on the Tracking Any Point (TAP) problem, reaching performance competitive with supervised methods.
- Global matching transformer architectures are effective for dense video correspondence, inherently encouraging smoother motion fields.
- The proposed 'label warping' is a critical technique for robust self-supervised training of transformer-based models, effectively mitigating 'shortcut solutions' that rely on positional encodings.
Summary by alphaXiv.
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