Text-Vision Retrieval Framework for Custom Knowledge Without Fine-Tuning
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Solution Overview
Problem
Existing pre-trained multimodal models struggle to effectively integrate user-specific custom data, particularly in private domain knowledge settings, requiring resource-intensive fine-tuning and lacking efficient in-context learning solutions for text-vision retrieval.
Innovation Solution
A flexible and efficient multimodal text-vision retrieval framework that integrates user-specific custom knowledge without fine-tuning, utilizing a dual-branch design and customizable result ensemble strategy to support both general and custom knowledge searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-trained multimodal models are used for text-vision retrieval, then general retrieval capability is provided, but integration of user-specific custom data is ineffective and requires resource-intensive fine-tuning
Solution Approach 1:
The patent introduces an intermediary component (projection layer/adaptor) that bridges the pre-trained multimodal model and user-specific custom data without requiring fine-tuning of the entire model. This intermediary enables effective integration of custom data while avoiding the resource-intensive fine-tuning process, directly resolving the contradiction between adaptability and energy consumption.
Solution Approach 2:
The patent segments the system into distinct components: the pre-trained multimodal model handles general retrieval capabilities, while a separate projection layer or adaptor module handles user-specific custom data integration. This segmentation allows each component to specialize in its function, achieving both general capability and custom adaptability without requiring resource-intensive fine-tuning of the entire system.
2Reliability
If fine-tuning is applied to integrate custom data, then user-specific knowledge is incorporated, but computational resources and time are significantly consumed
Solution Approach 1:
The patent extracts only the necessary components for custom data integration (projection layer, adaptor) from the full fine-tuning process. By taking out just the essential elements needed to incorporate user-specific knowledge, the system achieves reliable custom data integration while dramatically reducing the time and computational resources required compared to complete model fine-tuning.
Solution Approach 2:
The patent performs preliminary actions by pre-training the projection layer or adaptor on user-specific custom data before deployment. This preliminary integration of user-specific knowledge allows the system to achieve high reliability in custom data handling without requiring time-consuming fine-tuning during actual operation, as the adaptation is already accomplished in advance.
3Device complexity
If a unified model is used for both general and custom knowledge, then model simplicity is maintained, but performance on custom domain tasks deteriorates
Solution Approach 1:
The patent applies local quality by making different parts of the system serve different functions: the pre-trained multimodal model maintains simplicity for general tasks, while the added projection layer or adaptor provides specialized handling for custom domain tasks. This local differentiation ensures high accuracy on custom domain retrieval without significantly increasing overall model structural complexity.
Solution Approach 2:
The patent adds another dimension to the model architecture by introducing a projection layer or adaptor that operates in a separate computational space. This dimensional addition allows the system to handle custom domain tasks with high precision while keeping the original multimodal model structure relatively simple, effectively resolving the contradiction between complexity and precision.
4Adaptability or versatility
If resource-intensive fine-tuning is performed, then custom data integration is achieved, but computational cost and energy consumption increase
Solution Approach 1:
The patent employs a lightweight projection layer or adaptor that acts as a disposable or easily replaceable component for integrating custom data. This cheap component achieves effective custom data integration without the high energy consumption of fine-tuning the entire model, directly addressing the contradiction between adaptability and energy loss by using a computationally inexpensive solution.
Data Source
AI summary
A method for managing a framework includes: receiving, by a GUI, a query that is sent to a first module; analyzing, by the first module, the query to infer intention; making, by the first module, a determination that the intention is not searching for an object in a database; sending, by the first module, the query to a second module; transforming, by the second module, the query into a vector that is sent to a third module; performing, by the third module and using the vector, a search for a nearest image in the database; identifying, by the third module, a path associated with the nearest image, which is sent to an analyzer, in which the analyzer further sends the path to a fourth module; and fetching, by the fourth module and based on the path, an image from a store, which is sent to the GUI.