Semantic-Guided Diffusion Image Augmentation for Stable AI Training
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Solution Overview
Problem
Conventional image augmentation using generative models lacks controllability and accuracy, leading to unstable generation results and affecting the generalization and accuracy of artificial intelligence models.
Innovation Solution
An image augmentation device and method that utilizes a diffusion model to generate a generated image from a noise image and semantic information, incorporating semantic categories and ranges, and composites the generated image with guide images to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a text prompt is input into the generative model for image augmentation, then the generation process is simple, but the controllability and accuracy of the augmentation are poor
Solution Approach 1:
The patent segments the image into multiple semantic regions using semantic segmentation, allowing different parts of the image to be processed independently with region-specific text prompts. This enables precise control over which objects or regions are augmented and how, resolving the contradiction between simple operation and accurate control by adding semantic structure without significantly complicating the workflow
Solution Approach 2:
The patent applies different text prompts and augmentation parameters to different semantic regions of the image rather than treating the entire image uniformly. This allows locally optimized control where each region receives appropriate augmentation based on its semantic content, achieving high accuracy while maintaining ease of operation through automated region-specific processing
2Productivity
If the generative model generates content based on text prompt without semantic guidance, then the process is fast, but the generation result is unstable and inaccurate
Solution Approach 1:
The patent performs semantic segmentation and region identification before the actual image generation process. This preliminary action provides the generative model with structured guidance about which regions to modify and what content to generate, ensuring stable and accurate results while maintaining efficiency through pre-computed semantic information
Solution Approach 2:
The patent introduces semantic segmentation maps and region masks as intermediary structures between the text prompt and the generative model. These intermediaries translate high-level text descriptions into precise spatial and semantic constraints, enabling the model to generate stable and accurate content in the correct regions without requiring complex direct control mechanisms
3Manufacturing precision
If semantic segmentation is performed to guide the diffusion model, then the accuracy of content augmentation is improved, but the device complexity increases
Solution Approach 1:
The patent employs a diffusion model that can handle both the semantic understanding and the image generation tasks within a single unified framework. This multi-functional approach reduces overall system complexity by eliminating the need for separate specialized modules for each task, while still achieving high accuracy through semantic guidance
Solution Approach 2:
The patent combines the semantic segmentation module with the diffusion model into an integrated system where the segmentation information is directly fed into the diffusion process. This merging reduces the complexity of coordinating multiple independent components while maintaining the accuracy benefits of semantic guidance through seamless information flow between modules
Data Source
AI summary
An image augmentation device and method are provided. The image augmentation device inputs a noise image corresponding to an original image and semantic information and a text vector corresponding to the original image into a diffusion model to generate a generated image, and the generated image includes a partial contour of the original image. The image augmentation device composites the generated image and a plurality of guide images to generate an augmented image.