AI Image Generation With Merged Feedback Images for Contextual Refinement
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Solution Overview
Problem
Existing image search tools fail to provide customized and contextually relevant images that match the true intent and context of a user's search query, as they interpret keywords literally without understanding the relationships between keywords and context.
Innovation Solution
The system uses a text-to-image conversion tool that analyzes the context of a user's query prompt by understanding the relationships between keywords and context, generating an image that represents the user's intent, and allowing users to fine-tune image features based on style specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a text-to-image conversion tool interprets keywords literally to generate images, then the image generation process is simple and fast, but the generated images do not contextually match the user's true search intent
Solution Approach 1:
The patent introduces an image encoder as an intermediary component that processes the initial generated image and user feedback images to create a refined representation. This encoder acts as a mediator between the simple keyword interpretation and the complex contextual understanding requirements, enabling the system to generate images that better match user intent while maintaining reasonable processing efficiency
Solution Approach 2:
The system implements a feedback mechanism where user feedback images are incorporated into the generation process. The image encoder receives both the initially generated image and user feedback images, using the feedback to refine and adjust the final generated image to better align with the user's true search intent and contextual requirements
2Measurement precision
If the system analyzes context and relationships between keywords to generate customized images, then the contextual accuracy of generated images improves, but the device complexity increases
Solution Approach 1:
The image encoder serves multiple functions within the system: it processes the initial generated image, incorporates user feedback images, creates refined representations, and outputs final customized images. This multi-functionality reduces the need for separate specialized components for each processing stage, thereby managing system complexity while achieving high contextual accuracy
3Measurement precision
If multiple images are merged as input to an AI image generation algorithm, then the customization and contextual relevance of generated images improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of multiple input images (initial generated image and user feedback images) through the image encoder before final image generation. This preliminary action organizes and refines the input data structure, enabling more efficient processing during the actual image generation stage and reducing overall processing time despite the increased complexity of handling multiple images
Data Source
AI summary
Methods and system for fine tuning an image generated by an image generation artificial intelligence process includes receiving a generated image for a user prompt. The generated image is analyzed to identify image features included within. The identified image features are presented on a user interface for user selection for fine tuning. Selection of an image feature at the user interface is detected and an adjusted image is generated by fine tuning the selected image feature in accordance to tuning comments so that the image feature exhibits a style expressed by the user. The adjusted image is returned to the client device for rendering.