Question-Answer Verification for Semantically Accurate Image Search
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Solution Overview
Problem
Existing image search methods using large models suffer from semantic inconsistencies between input information and output images, leading to low accuracy in capturing subtle image details and complex semantic relationships.
Innovation Solution
An image search method involving a first large model to generate question-answer pairs from input text, followed by a second large model for image-text analysis to match images with the search requirement, ensuring precise semantic alignment through comparison of answer information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image search is performed using large models, then image retrieval can be conducted, but semantic inconsistency occurs between input text and output images
Solution Approach 1:
The patent segments the image search process into multiple stages: initial image retrieval using a first large model, semantic analysis to generate question-answer pairs, and verification using a second large model. This segmentation allows each stage to focus on specific aspects, improving overall semantic consistency while maintaining retrieval efficiency.
Solution Approach 2:
The patent implements a feedback mechanism where the second large model verifies the semantic consistency between retrieved images and input text by comparing generated answers with ground truth. This feedback loop identifies and corrects semantic inconsistencies, improving the accuracy of image retrieval results.
2Measurement precision
If semantic analysis is performed to improve accuracy, then semantic consistency improves, but processing time increases
Solution Approach 1:
The patent performs preliminary semantic analysis using the first large model to generate question-answer pairs before final verification. This preliminary action prepares the necessary semantic representations in advance, reducing the computational burden during the verification stage and overall processing time.
Solution Approach 2:
The patent applies partial action by using the second large model for verification only on critical semantic aspects rather than re-processing the entire image retrieval process. This selective verification approach maintains high semantic consistency while minimizing additional processing time.
3Measurement precision
If multiple large models are used for analysis, then semantic consistency improves, but system complexity increases
Solution Approach 1:
The patent employs multi-functional large models where the first large model performs both initial retrieval and semantic analysis, while the second large model handles verification. This universal approach allows a single model to perform multiple functions, reducing the need for specialized separate systems and simplifying overall architecture.
Solution Approach 2:
The patent introduces question-answer pairs as an intermediary representation between the input text and candidate images. This intermediary layer simplifies the interaction between different model components, allowing each model to focus on specific tasks without direct complex interactions, thereby reducing system architecture complexity.
Data Source
AI summary
An image search method, an intelligent agent, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence technology. The method includes: acquiring at least one first candidate image matched with an input text information; performing a semantic analysis on the input text information by using a first large model to generate at least one question-answer pair which includes a question information and a first answer information; performing an image-text analysis on the question information and the at least one first candidate image by using a second large model to generate a second answer information for answering each question information; and determining at least one target image matched with the image search requirement from the at least one first candidate image according to a comparison result between the at least one first answer information and the at least one second answer information.