18078402. INTEGRATING MODEL REUSE WITH MODEL RETRAINING FOR VIDEO ANALYTICS simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)
Contents
- 1 INTEGRATING MODEL REUSE WITH MODEL RETRAINING FOR VIDEO ANALYTICS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INTEGRATING MODEL REUSE WITH MODEL RETRAINING FOR VIDEO ANALYTICS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
INTEGRATING MODEL REUSE WITH MODEL RETRAINING FOR VIDEO ANALYTICS
Organization Name
MICROSOFT TECHNOLOGY LICENSING, LLC
Inventor(s)
Ganesh Ananthanarayanan of Sammamish WA (US)
Yuanchao Shu of Kirkland WA (US)
Paramvir Bahl of Bellevue WA (US)
Tsuwang Hsieh of Sammamish WA (US)
INTEGRATING MODEL REUSE WITH MODEL RETRAINING FOR VIDEO ANALYTICS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18078402 titled 'INTEGRATING MODEL REUSE WITH MODEL RETRAINING FOR VIDEO ANALYTICS
Simplified Explanation
The abstract describes a system and method for reusing and retraining an image recognition model for video analytics. The technology involves selecting a model from a model store, using a gating network model to determine candidate models for validation, and retraining the model until the rate of accuracy improvement becomes small.
- Selecting image recognition models for reuse or retraining
- Using a gating network model for model selection
- Retraining the model until accuracy improvement rate decreases
Potential Applications
This technology can be applied in various fields such as surveillance, security, autonomous vehicles, and industrial automation for real-time video analytics and object recognition.
Problems Solved
1. Efficient reuse and retraining of image recognition models 2. Improving accuracy of video analytics systems
Benefits
1. Increased accuracy and efficiency in video analytics 2. Cost-effective solution for edge devices 3. Real-time object recognition capabilities
Potential Commercial Applications
Optimizing video surveillance systems for better security Enhancing autonomous vehicles with improved object recognition capabilities
Possible Prior Art
Prior art in the field of machine learning and computer vision may include similar methods for retraining models and improving accuracy in image recognition systems.
What is the impact of this technology on edge devices?
The technology allows edge devices to efficiently process video data by reusing and retraining image recognition models, leading to improved accuracy and reduced computational load on the devices.
How does the gating network model contribute to model selection?
The gating network model helps in selecting ranked candidate models for validation, ensuring that only the most suitable models are chosen for reuse or retraining, thereby optimizing the overall performance of the system.
Original Abstract Submitted
Systems and methods are provided for reusing and retraining an image recognition model for video analytics. The image recognition model is used for inferring a frame of video data that is captured at edge devices. The edge devices periodically or under predetermined conditions transmits a captured frame of video data to perform inferencing. The disclosed technology is directed to select an image recognition model from a model store for reusing or for retraining. A model selector uses a gating network model to determine ranked candidate models for validation. The validation includes iterations of retraining the image recognition model and stopping the iteration when a rate of improving accuracy by retraining becomes smaller than the previous iteration step. Retraining a model includes generating reference data using a teacher model and retraining the model using the reference data. Integrating reuse and retraining of models enables improvement in accuracy and efficiency.
