Source-linked AI summary
Tracking Emerges by Colorizing Videos
Carl Vondrick, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, Kevin Murphy
TL;DR
Large-scale visual tracking usually depends on costly labeled datasets, so the paper asks whether unlabeled video can provide a self-supervised tracking signal. It trains a colorization model to copy colors from a reference frame, using its learned pointing mechanism as a tracker. Experiments suggest tracking emerges automatically, outperforming optical-flow methods, while tracking failures correlate with colorization failures.
Problem
Large-scale tracking datasets require extensive and expensive annotation, motivating visual tracking learned from raw unlabeled video.
Method
The model learns video colorization by copying colors from a colorful reference frame, then reuses its learned pointing mechanism to propagate labels for tracking.
Results
Experiments suggest tracking emerges without ground-truth labels and performs well enough to outperform latest optical-flow methods across video segments and human pose tracking.
Takeaways & Limitations
Video colorization appears to be a promising signal for self-supervised learning of video models and tracking multiple kinds of annotations without retraining.
Takeaways & Limitations
The method assumes color is generally temporally stable, although colorful lights and other exceptions can violate this assumption.
Abstract
from arXiv · showhide
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.
1 Introduction
The paper learns visual tracking from unlabeled video by training a colorization model to copy colors from a reference frame. Experiments suggest tracking emerges automatically and performs competitively with optical-flow methods, while tracking and colorization failures are correlated.
- Large-scale tracking datasets require extensive, expensive annotation, motivating learning visual tracking from raw unlabeled video.
- The model colorizes grayscale frames by learning to copy colors from a reference frame, requiring it to point to corresponding regions.
- Experiments suggest that tracking emerges automatically despite training without ground-truth labels.
- The colorization model tracks video segments and human pose well enough to outperform the latest optical-flow methods.
- Tracking failures are often correlated with colorization failures, suggesting that improving video colorization may advance self-supervised tracking.
- The paper presents learning to colorize video as its main contribution for causing tracking to emerge and enabling tracking without further training.
2 Related Work
The paper situates its approach among self-supervised visual representation learning, colorization, video segmentation, and label-free tracking. It distinguishes training without labels from test-time tracking initialized with a labeled first frame.
- Self-supervised visual learning commonly exploits natural image and video context to learn representations for downstream tasks.
- Prior video-colorization methods use optical flow or color propagation for temporal coherence, whereas this paper leverages temporal color stability instead.
- Video segmentation is challenging and state-of-the-art methods typically rely on substantial supervision, unlike this approach using unlabeled video.
- The proposed generic tracker transfers segments, keypoints, colors, and other first-frame annotations without fine-tuning or retraining.
- The paper calls its method unsupervised because training uses only unlabeled video, although testing tracks a region specified in the first frame.
3 Self-supervised Tracking
The model learns a pointing-based colorization task from unlabeled video, then reuses its similarity matrix to propagate colors or labels for tracking. It supports segment and keypoint tracking, with design choices addressing ambiguity, long videos, and uncertain predictions.
- 3.1 Model: The model predicts target-frame colors as a linear combination of reference-frame colors using a row-normalized similarity matrix.The matrix uses inner-product similarity with softmax normalization over convolutional-network pixel embeddings.
- 3.1 Model: Because softmax can point to one reference pixel, objects sharing a color need not share embeddings, enabling tracking of multiple same-colored objects.The model uses a pointing mechanism related to attention, matching, and pointer networks, but trains it without supervision for visual tracking.
- 3.1 Model: The approach assumes color is generally temporally stable in unlabeled web video, while acknowledging exceptions such as lights turning on and off.Frames from Kinetics visualize this temporal color coherence, which supplies large-scale training data for tracking.
- 3.2 Learning: Training optimizes color predictions against target colors with a categorical cross-entropy loss after quantizing Lab ab channels into 16 clusters.The model is trained on unlabeled videos; color quantization uses k-means clustering across the training set.
- 3.3 Inference: At inference, the learned similarity matrix propagates distributions of labels from an initially labeled frame, supporting segment and keypoint tracking without changing the label-space dimension.Segments use category-probability vectors, while keypoints are converted into dense binary representations; long videos use recursive propagation over a window of three frames.
- 3.4 Implementation Details: The tracker outperforms optical-flow methods on DAVIS 2017 video segmentation but does not yet match heavily supervised training.The model also colorizes across challenging transformations, while inference uses temperature T = 0.5 to make predictions more confident.
4 Experiments
Experiments test whether a tracker emerges from video colorization using unlabeled Kinetics video, across video segmentation and human pose tracking. The model is compared with identity, optical-flow, single-image colorization, and supervised baselines, including analyses of motion attributes, duration, qualitative behavior, and failure correlation.
- Experimental Setup: The experiments evaluate whether a tracker emerges automatically from video colorization, using video segmentation and human pose tracking applications.The model is trained on unlabeled Kinetics video and evaluated on standard testing sets for each task.
- Experimental Setup: The comparison includes identity, classical and learned optical-flow, single-image colorization, and supervised tracking baselines.Optical-flow baselines warp initial labels between frames, while supervised methods use labeled datasets and first-frame fine-tuning.
- Video Segmentation: The colorization model outperforms optical-flow methods on DAVIS video segmentation, where occlusion, deformation, scale change, and motion blur challenge label warping.The authors suggest end-to-end learning of the warping mechanism on videos containing these effects may explain the advantage.
- Pose Tracking: For human pose tracking, performance is similar to optical flow at strict thresholds but better at relaxed thresholds, indicating less severe localization errors.Qualitative results show large motions are often tracked, while occlusion can cause keypoints to be unrecoverable.
- Video Segmentation: Performance remains more consistent over longer videos than optical flow, which eventually degrades to the identity baseline as errors accumulate.The model also drifts, but remains relatively stronger for longer durations.
- Video Segmentation: The model shows relative strengths over optical flow for dynamic backgrounds, fast motion, occlusion, and motion blur, while qualitative examples show tracking of multiple instances and large deformations.A multiple-object evaluation reports segment overlap J of 31.0 versus 19.1 for optical flow; typical failures involve small objects and fine-grained detail.
- Model Analysis: Tracker failures have a moderate association with colorization failures, with an odds ratio of 2.3, suggesting video-colorization improvements may improve tracking.The learned embeddings also tend to place object instances near one another despite deformations and viewpoint changes.
5 Conclusion
Video colorization provides a promising signal for learning visual tracking without human supervision. The resulting tracker supports video segmentation and human pose tracking, and improved colorization may improve self-supervised tracking.
- Video colorization by pointing to a colorful reference frame causes a visual tracker to emerge automatically.
- The emergent tracker can be applied to video segmentation and human pose tracking.
- Abundant unlabeled full-color video makes video colorization a powerful signal for self-supervised learning of video models.
A Performance by Attribute
Performance is broken down by motion attributes, with attributes sorted by relative gain over optical flow. The reproduced table directs readers to Section 4.3 for interpretation.
- Average performance is broken down by attributes describing the type of motion in each video.
- The attributes are sorted by relative gain over optic flow.
B Network Architecture
The model processes four down-sampled grayscale frames through shared-frame feature extraction and a 3D spatiotemporal convolutional network. It produces spatial feature maps and embeddings for the video inputs.
- Four grayscale video frames are down-sampled to 256×256 and processed by a 3D convolutional network.The network produces 64-dimensional embeddings and a 32 × 32 feature map for each input frame.
- A shared-weight ResNet-18 processes each input frame and outputs a 32 × 32 spatial map with 256 dimensions per location.The fully connected layers and global average pooling are removed, and the output stride is modified.
- The ResNet-18 features are fed into a 3D spatiotemporal convolutional network using Time × Width × Height notation.
- Each convolution is followed by batch normalization and ReLU, except the final embedding-producing layer.The implementation uses TensorFlow.