Aesthetic Image Retrieval Using Intent-Refined Visual Search
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Solution Overview
Problem
Existing visual content retrieval systems face inefficiencies due to complex architectures that require significant computing resources and overlook user intent, leading to computational inefficiencies and lack of personalized, context-aware search experiences.
Innovation Solution
A streamlined visual content retrieval system using a generative language model and Approximate k-Nearest Neighbor (ANN) search techniques, combined with reinforcement learning to generate refined queries and prioritize aesthetic content, incorporating user intent and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex multi-stage model architectures are used, then retrieval performance and user intent understanding are improved, but system complexity and computational resource requirements increase
Solution Approach 1:
The patent segments the retrieval system into distinct functional modules: a query processing module that handles user intent analysis, a visual content retrieval model for aesthetic content selection, and an indexing system. This modular segmentation allows each component to specialize in specific tasks, improving overall retrieval performance while managing system complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces an intermediary query processing module that acts as a mediator between the user's initial query and the visual content retrieval model. This intermediary analyzes user intent and refines queries before they reach the retrieval model, enabling better user intent understanding without requiring the retrieval model itself to be overly complex.
2Measurement precision
If complex multi-stage model architectures are used, then retrieval performance is improved, but computing resources and deployment difficulty increase
Solution Approach 1:
The patent applies preliminary action by pre-processing queries in the query processing module before they reach the retrieval model. User intent is analyzed and queries are refined in advance, which reduces the computational burden on the retrieval model during actual retrieval operations, thereby reducing overall computing resource requirements while maintaining performance.
Solution Approach 2:
The patent applies local quality by assigning specialized processing capabilities to specific modules: the query processing module handles linguistic analysis and intent understanding, while the retrieval model focuses on visual content matching. This localized specialization allows each component to be optimized for its specific function, improving overall efficiency without requiring all components to be maximally complex.
3Device complexity
If single-stage model architectures are used, then system complexity and computing resources are reduced, but user intent and context understanding capability deteriorate
Solution Approach 1:
The patent segments the system into a query processing module for intent analysis and a retrieval model for content selection. This segmentation enables the system to maintain low overall complexity while assigning specific intent understanding capabilities to the dedicated query processing module, thus resolving the trade-off between simplicity and adaptability.
Solution Approach 2:
The query processing module serves multiple functions: it analyzes user intent, refines queries, and prepares inputs for the retrieval model. This multi-functionality allows a single module to handle various aspects of user interaction, improving adaptability without requiring multiple specialized components that would increase system complexity.
4Ease of manufacture
If single-stage model architectures are used, then deployment and maintenance are simplified, but personalized and context-aware search capability is lost
Solution Approach 1:
The patent segments the system into independent modules with well-defined interfaces, which simplifies deployment and maintenance while enabling specialized functionality. The query processing module can be developed, tested, and deployed independently from the retrieval model, making the overall system easier to maintain despite its enhanced capability for personalized search.
Solution Approach 2:
The query processing module acts as an intermediary that adds personalized and context-aware capabilities without requiring changes to the core retrieval model. This intermediary layer enables advanced functionality while keeping the underlying retrieval system simple and easy to deploy, as the complexity is isolated to the query processing layer.
Data Source
AI summary
A method of retrieving visual content includes receiving user input defining an initial search query from a client application. The initial search query and a meta prompt are then delivered to a refined query generating model which is trained to analyze the initial search query to determine user intent and to generate a refined search query based on the initial search query and the meta prompt. The refined search query is delivered to a visual content retrieval model which retrieves aesthetic visual content with reference to a visual content index. Retrieved aesthetic visual content is returned to the client application.