Optical Flow Model Adaptation with Compressed Video Motion Vectors
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Solution Overview
Problem
Existing DNN-based optical flow estimation (OFE) models face challenges in robustness due to test distribution shift, requiring large-scale ground-truth data and being costly and time-consuming to adapt to real-world scenarios, and lack effective test-time adaptation methods.
Innovation Solution
A two-headed multi-level machine learning model architecture with self-supervised motion vector (MV) prediction and meta-learning processes, utilizing MV maps from compressed video streams for test-time adaptation, allowing models to adapt to new distributions with limited labeled samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional iterative algorithms are used for optical flow estimation, then brightness constancy constraint can be enforced, but the computation speed is slow and convergence is not guaranteed
Solution Approach 1:
The patent replaces traditional iterative mechanical optimization algorithms with a deep neural network-based end-to-end learning system. The network directly learns the mapping from image pairs to optical flow maps, substituting the iterative constraint-satisfaction approach with a trained model that provides both speed and reliability.
Solution Approach 2:
The patent transforms the optimization problem into a parameter learning problem. Instead of iteratively adjusting optical flow parameters to satisfy brightness constancy, the system learns optimal parameters during training by minimizing a composite loss function that includes brightness constancy, smoothness, and data consistency terms.
2Measurement precision
If DNN-based optical flow models are trained on large-scale ground-truth data, then prediction accuracy improves, but adaptation to real-world scenarios becomes costly and time-consuming
Solution Approach 1:
The patent performs preliminary adaptation during the training phase by incorporating domain-specific loss functions and augmentation strategies. This preliminary action prepares the model for real-world scenarios, reducing the need for costly post-training adaptation while maintaining high prediction accuracy.
Solution Approach 2:
The system uses self-supervised learning components that allow the model to learn from unlabelled real-world data. By incorporating consistency regularization and self-training mechanisms, the model adapts to new domains using its own predictions, reducing dependency on expensive ground-truth annotations.
3Reliability
If test-time adaptation is implemented for distribution shift, then robustness improves, but computational overhead increases
Solution Approach 1:
The patent pre-computes adaptation parameters and domain-invariant features during the training phase. This preliminary action enables the model to quickly adapt to distribution shifts at test time without requiring heavy computational resources, as the bulk of the adaptation work is done beforehand.
Solution Approach 2:
The system implements selective adaptation by focusing only on the most critical aspects of distribution shift. Rather than fully re-adapting all model parameters at test time, it applies partial adaptation to specific layers or parameters that are most sensitive to domain changes, reducing computational overhead while maintaining robustness.
4Loss of energy
If video compression schemes are used for efficient transmission, then bandwidth usage decreases, but motion vector information becomes lossy and less accurate
Solution Approach 1:
The patent uses the compressed motion vectors as an intermediary signal rather than treating them as ground truth. The system incorporates these lossy motion vectors into the training process as additional constraints or priors, allowing the model to learn from both accurate ground-truth data during training and compressed video data during adaptation, thus bridging the accuracy gap.
Data Source
AI summary
There is provided a method, apparatus and system for adapting a machine learning model for optical flow prediction. A machine learning model can be trained or adapted based on compressed video data, using motion vector information extracted from the compressed video data as ground-truth information for use in adapting the model to a motion vector prediction task. The model so adapted can accordingly be adapted for the similar task of optical flow prediction. Thus, the model can be adapted at test time to image data which is taken from an appropriate distribution. A meta-learning process can be performed prior to such model adaptation to potentially improve the model's performance.