Grounded Image Querying for Accurate Responses with Fewer Model Calls
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Solution Overview
Problem
Existing large generative models (LGMs) suffer from inaccuracies and inefficiencies, particularly when describing unique or recently posted images, often requiring users to provide additional information or indicating unavailability of accurate responses.
Innovation Solution
An image query system that utilizes a combination of visual-based and reverse image search grounding information with a large generative language model to generate accurate text responses, minimizing calls to the computationally expensive visual-based model by using a comprehensive image prompt and threshold confidence checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a visual-based large generative model is used to describe images, then response accuracy is improved, but computational cost and time consumption increase
Solution Approach 1:
The system performs reverse image search in advance to retrieve grounding information about the input image before the user asks questions. This preliminary action stores relevant image context, metadata, and descriptive information that can be quickly accessed during subsequent queries, eliminating the need for repeated expensive visual model calls while maintaining high response accuracy.
Solution Approach 2:
Instead of repeatedly calling the expensive visual-based large generative model, the system creates a copy of the image analysis results through reverse image search. The grounding information obtained from reverse image search serves as a surrogate that can be quickly processed by the language model, reducing computational cost while preserving accuracy for common image descriptions.
2Measurement precision
If multiple visual-based large generative model calls are made for different queries, then response accuracy is improved, but processing time increases
Solution Approach 1:
The system performs reverse image search in advance to retrieve grounding information about the input image before the user asks questions. This preliminary action stores relevant image context, metadata, and descriptive information that can be quickly accessed during subsequent queries, eliminating the need for repeated expensive visual model calls while maintaining high response accuracy.
Solution Approach 2:
Once the reverse image search retrieves grounding information for an image, this information remains available for all subsequent queries about the same image. The useful action of image analysis continues without interruption through the language model's ability to reference the stored grounding information, eliminating repeated computational overhead.
3Measurement precision
If reverse image search grounding information is used, then response accuracy for unique images is improved, but system complexity increases
Solution Approach 1:
The reverse image search grounding information serves multiple functions: it provides image description, identifies objects and concepts, extracts metadata, and supports various types of queries about the same image. This multi-functional grounding information reduces the need for multiple specialized models, simplifying the overall system architecture while improving accuracy for unique images.
Data Source
AI summary
The disclosure describes utilizing an image query system to improve response accuracy and reduce computational steps resources in responding to natural language queries of input images. In various implementations, the image query system utilizes grounding information from one or more sources to determine accurate information for an input image. For example, the image query system uses a single comprehensive image prompt to obtain extensive visual image grounding information for the input image from a visual-based generative AI model. Additionally, or in alternative implementations, the image query system obtains reverse image search grounding information for the input image. The image query system then cleverly utilizes the grounding information with a generative AI model to generate text query responses to image-based queries of the input image more accurately and efficiently.