Papers › Tracking Emerges by Colorizing Videos
Tracking Emerges by Colorizing Videos
Carl Vondrick, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, Kevin Murphy
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Skeleton Based Action Recognition | JHMDB Pose Tracking | ColorPointer | PCK@0.1 | 45.2 | #2 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | JHMDB Pose Tracking | ColorPointer | PCK@0.2 | 69.6 | #2 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | JHMDB Pose Tracking | ColorPointer | PCK@0.3 | 80.8 | #2 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | JHMDB Pose Tracking | ColorPointer | PCK@0.4 | 87.5 | #2 of 3 | Archive leaderboard | report |
| Skeleton Based Action Recognition | JHMDB Pose Tracking | ColorPointer | PCK@0.5 | 91.4 | #2 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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