Papers › Tracking Emerges by Colorizing Videos

Tracking Emerges by Colorizing Videos

25 Jun 2018ECCV 2018 9arXiv:1806.09594archive 2025-07-28

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

ColorizationOptical Flow EstimationSkeleton Based Action RecognitionVisual Tracking

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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Methods

Colorization

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