We use large amounts of unlabeled video to learn models for visual tracking
without manual human supervision. We leverage the natural temporal coherency of
color to create a model that learns to colorize gray-scale videos by copying
colors from a reference frame. Quantitative and qualitative experiments suggest
that this task causes the model to automatically learn to track visual regions.
Although the model is trained without any ground-truth labels, our method
learns to track well enough to outperform the latest methods based on optical
flow. Moreover, our results suggest that failures to track are correlated with
failures to colorize, indicating that advancing video colorization may further
improve self-supervised visual tracking.