Multi-Modality Image Training Parameter Adjustment
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Solution Overview
Problem
Current machine learning systems face challenges in effectively training models using multi-modality data, particularly in image recognition tasks, where varying modalities such as visible light and infrared images present different parameters and require accurate parameter modification to enhance model accuracy.
Innovation Solution
A system comprising a device, management node, and server configured to communicate and process multiple image modalities, where the management node trains and tests machine learning models by modifying image modality parameters based on similarity and accuracy thresholds, selecting the most accurate model for actions like image recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained using multi-modality training data with varying parameters, then the model accuracy and performance are improved, but the complexity of data processing and parameter management increases
Solution Approach 1:
The patent applies parameter changes by systematically varying training parameters such as image size, color space, file format, and compression quality across different modalities. The system modifies these parameters to create diverse training datasets while managing the complexity through automated parameter variation strategies, allowing models to learn robust features without manual intervention for each parameter combination.
Solution Approach 2:
The training system is designed with multi-functionality to handle multiple image modalities (visible light, infrared, thermal) simultaneously with a unified processing framework. The system can process various file formats, color spaces, and resolution levels through a single versatile pipeline, reducing the need for separate processing systems for each modality while maintaining high model accuracy.
2Adaptability or versatility
If multiple image modalities with different parameters are used for training, then the system's ability to recognize objects in various image types is enhanced, but the difficulty of detecting and measuring parameters accurately increases
Solution Approach 1:
The patent introduces an intermediary processing layer that standardizes parameter representation across different image modalities. This intermediary layer converts various image formats, color spaces, and resolutions into a unified parameter space, making it easier to detect and measure parameters accurately while maintaining the ability to process diverse multi-modal data for enhanced recognition capability.
3Reliability
If image modality parameters are modified based on similarity and accuracy thresholds, then the quality of training data is improved, but the time required for parameter modification and model training increases
Solution Approach 1:
The system performs preliminary parameter modification based on pre-defined similarity and accuracy thresholds before actual model training begins. By pre-processing the training data to adjust parameters according to established criteria, the system ensures high training data quality while reducing the time required during the actual training phase, as the parameter optimization work is completed in advance.
Data Source
AI summary
A management node is described. A method implemented in a management node configured for training and testing one or more machine learning (ML) models. The method comprises training a first ML model using a plurality of image modalities as training data. The plurality of image modalities comprises a first image modality and a second image modality different from the first image modality. The first image modality has a first image modality parameter and the second image modality has a second image modality parameter. The method further includes modifying one or both of the first image modality parameter and the second image modality parameter, training a second ML model using the plurality of image modalities and the modified one or both of the first image modality parameter and the second image modality parameter, and testing the first ML model and the second ML model based on an accuracy threshold.