Dual-Diffusion Image Bridging for Metadata-Guided AI Datasets
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Solution Overview
Problem
Existing methods for training large datasets are inefficient and costly, and the technical problem is that existing methods for generating high-quality, diverse datasets for training object detection systems are inefficient and costly, and the technical problem is that existing methods of manually taking pictures of the target objects using cameras and having a human operator label each image with a ground truth target object class are not scalable and slow the development and deployment of large scale object detection systems.
Innovation Solution
The technical solution is the utilization of meta data from Design, Production, and Inspection data in manufacturing and production environments the illustrative embodiments control output of the generative AI models through Dual-Fusion techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual picture taking and human labeling methods are used to develop training datasets, then high accuracy and specificity can be achieved, but productivity is reduced and loss of time increases
Solution Approach 1:
The patent uses generative AI models to create synthetic copies of training images and labels automatically, replacing manual photographing and human labeling. The system generates realistic training data with ground truth annotations through automated image synthesis, eliminating the need for manual picture taking and human operator labeling while maintaining data quality and diversity.
2Adaptability or versatility
If large realistic visual datasets are developed using conventional manual methods, then high diversity can be achieved, but device complexity and cost increase
Solution Approach 1:
The system uses automated generative AI models that self-generate diverse training datasets without requiring complex manual intervention systems. The AI models automatically create varied images across different environments, lighting conditions, and object configurations, eliminating the need for extensive manual setup and coordination while maintaining high dataset diversity.
3Measurement precision
If conventional manual methods are used for dataset development, then high specificity can be achieved, but loss of time increases
Solution Approach 1:
The system performs preliminary automated generation of specific training images with precise labels before they are needed for model training. The generative AI models pre-create diverse, specific images with ground truth annotations in advance, eliminating the time-consuming manual process of photographing and labeling each image individually while maintaining the required specificity for accurate object detection training.
Data Source
AI summary
Semi-generative artificial intelligence modelling between domains is provided. The method comprises receiving a source image of an object in a first domain and diffusing the source image through a source diffusion model to generate a first Gaussian distribution in the first domain. Embeddings are generated from metadata which provides constraints for image reconstruction. The embeddings are fed into dual diffusion implicit bridges. The first Gaussian distribution is sampled and mapped, through the dual diffusion implicit bridges, from the first Gaussian distribution to a second Gaussian distribution in a second domain. The second Gaussian distribution is then reversed diffused through a target diffusion model to generate a target image of the object in the second domain in accordance with the metadata.