17524480. METHOD, APPARATUS AND SYSTEM FOR ADAPTATING A MACHINE LEARNING MODEL FOR OPTICAL FLOW MAP PREDICTION simplified abstract (Huawei Technologies Co., Ltd.)
Contents
- 1 METHOD, APPARATUS AND SYSTEM FOR ADAPTATING A MACHINE LEARNING MODEL FOR OPTICAL FLOW MAP PREDICTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD, APPARATUS AND SYSTEM FOR ADAPTATING A MACHINE LEARNING MODEL FOR OPTICAL FLOW MAP PREDICTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
METHOD, APPARATUS AND SYSTEM FOR ADAPTATING A MACHINE LEARNING MODEL FOR OPTICAL FLOW MAP PREDICTION
Organization Name
Inventor(s)
Seyed Mehdi Ayyoubzadeh of Hamilton (CA)
Irina Kezele of North York (CA)
METHOD, APPARATUS AND SYSTEM FOR ADAPTATING A MACHINE LEARNING MODEL FOR OPTICAL FLOW MAP PREDICTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17524480 titled 'METHOD, APPARATUS AND SYSTEM FOR ADAPTATING A MACHINE LEARNING MODEL FOR OPTICAL FLOW MAP PREDICTION
Simplified Explanation
The patent application describes a method, apparatus, and system for adapting a machine learning model for optical flow prediction.
- The machine learning model is trained or adapted using compressed video data.
- Motion vector information extracted from the compressed video data is used as ground-truth information to adapt the model for motion vector prediction.
- The adapted model can then be used for optical flow prediction by adapting it at test time to image data from an appropriate distribution.
- Prior to model adaptation, a meta-learning process can be performed to potentially improve the model's performance.
Potential Applications
- Video compression and encoding
- Video analysis and understanding
- Computer vision applications
- Autonomous vehicles and drones
- Virtual reality and augmented reality
Problems Solved
- Improves the accuracy and efficiency of optical flow prediction
- Enables adaptation of machine learning models for specific tasks using compressed video data
- Reduces the need for extensive labeled training data
Benefits
- More accurate and reliable optical flow prediction
- Faster and more efficient processing of video data
- Adaptation of machine learning models for specific tasks without extensive retraining
- Improved performance of computer vision applications in various domains
Original Abstract Submitted
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.
