Image Context Text Generation for Reliable Text-Image Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing text generation tools fail to account for visual elements of images, resulting in generic or mismatched text that does not capture the unique context and intended use of the image, hindering the creation of cohesive and engaging content.
Innovation Solution
A system that leverages a Large Language Model (LLM) to analyze both the visual context of an image and user intent, generating text that is contextually relevant and aligned with the image's mood, theme, or activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional text generation methods are used, then text can be generated quickly based on textual input, but the text does not align with visual context
Solution Approach 1:
The patent combines text generation capability with image analysis capability into a single integrated system. The text generation model receives both textual prompts and image inputs, merging two previously separate functions (text processing and image processing) into one unified system that produces text aligned with visual context.
Solution Approach 2:
The patent introduces an intermediary mechanism that processes image data and converts it into a format suitable for text generation. This intermediary layer analyzes image features, extracts relevant information, and transforms it into textual representations that can be processed by the language model, enabling the bridge between visual and textual domains.
2Reliability
If manual text crafting is required to align with images, then text-image alignment can be achieved, but content creation time increases
Solution Approach 1:
The system performs self-service by automatically analyzing the uploaded image and generating appropriate text descriptions without requiring manual intervention. The image analysis component autonomously extracts visual information, and the text generation model autonomously creates aligned text, eliminating the need for users to manually craft text that matches their image selections.
Solution Approach 2:
The system performs preliminary action by pre-processing the uploaded image to extract key visual features, objects, and contextual information before text generation occurs. This preliminary image analysis and feature extraction happens automatically in the background, preparing the necessary data structures that enable rapid, accurate text generation aligned with the visual content.
3Reliability
If image analysis is added to text generation, then contextual relevance improves, but processing time increases
Solution Approach 1:
The system applies partial action by selectively analyzing only the most relevant visual features of the image rather than processing every pixel and detail. The image analysis component identifies and extracts key objects, actions, and contextual elements that are most important for text generation, omitting redundant or less significant visual information to maintain processing efficiency.
Solution Approach 2:
The patent segments the image processing task into distinct functional modules: image loading, feature extraction, context analysis, and text generation. Each segment handles a specific aspect of image processing independently, allowing for optimized processing of each component and enabling parallel execution where appropriate, thereby reducing overall processing time while maintaining high contextual relevance.
Data Source
AI summary
Methods, systems, and storage media for generating contextually relevant text from image descriptions and user intent are disclosed. Exemplary implementations may: receive an image and a user-defined intent for text output; analyze the received image to generate a contextual description of the image; generate a query based on the contextual description of the image and the user-defined intent; and generate the text output based on the query.