Context-Aware Image Generation Device for News Content
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image processing technologies fail to generate appropriate content that accurately reflects the context of news articles, often neglecting to incorporate the article's context into the processed images.
Innovation Solution
A generation device and method that acquires content and policy information to modify specific parts of the content based on disclosure policies, using image recognition and reinforcement learning to generate modified content that aligns with the intended context indicated by user-inputted text, ensuring the content's intention is reflected and feels natural to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional image processing technology is used to generate processed images from news articles, then image processing can be performed, but the context of the news article cannot be reflected in the processed image
Solution Approach 1:
The patent segments the content processing into multiple stages: first extracting text information from the news article, then performing morphological analysis to identify topics, and finally generating processed images that reflect the extracted context. This segmentation allows context information to be preserved and reflected in the output images without overwhelming the system with undifferentiated complexity
Solution Approach 2:
The patent introduces an intermediary processing layer that includes morphological analysis and topic extraction components. This intermediary layer acts as a bridge between the raw news article and the final processed image, enabling context information to be extracted and transformed into visual representations without requiring direct complex interaction between all system components
2Reliability
If the content is modified to reflect user intent accurately, then the feeling of strangeness is reduced, but the processing complexity increases
Solution Approach 1:
The patent employs reinforcement learning mechanisms where the generation model produces modified content and receives feedback based on how well it reflects user intent. The system continuously adjusts its generation process based on this feedback, improving content appropriateness over time without requiring manual intervention for each modification
Solution Approach 2:
The generation model is designed to autonomously learn and improve its content generation capabilities through self-service mechanisms. The reinforcement learning framework allows the system to automatically optimize its own performance in generating context-appropriate content without external reprogramming or manual adjustment for each new context
Data Source
AI summary
A generation device according to the present application includes: an acquisition unit which acquires content targeted for disclosure (for example, an image which a user desires to disclose) and policy information which indicates a disclosure policy of the content (for example, a text which the user inputs as a caption of the image); and a generation unit which generates modified content in which a predetermined part of the content is modified based on the disclosure policy indicated by the policy information (for example, content in which an object identified based on the disclosure policy is emphasized).