Multi-modal Search Embedding Text and Image Vectors
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Solution Overview
Problem
Current search technologies for two-dimensional and three-dimensional objects in repositories are limited to text-based or image-based queries, failing to effectively handle multi-modal queries that combine images and text, particularly for three-dimensional object retrieval, due to the lack of unified representation across different modalities.
Innovation Solution
A method that embeds vector representations of image and tag data into a unified vector space, allowing for the combination of multi-modal data using vector calculus, enabling searching within a single space by leveraging semantic information and using neural networks for improved computational efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text-based search is used, then semantic similarity can be searched, but visual similarity search capability is lost
Solution Approach 1:
The patent merges text-based and image-based search modalities into a unified multi-modal search system. The system accepts both text queries and image queries, and can also process combined multi-modal queries, allowing users to switch between different search types or use them together for more precise results.
Solution Approach 2:
The search system is designed to handle multiple types of queries universally - text queries, image queries, and multi-modal queries. The underlying architecture processes all these query types through a common retrieval mechanism, making the system adaptable to different search needs while maintaining a single unified interface.
2Measurement precision
If image-based search is used, then visual similarity can be searched, but semantic understanding capability is reduced
Solution Approach 1:
The patent combines visual feature extraction with semantic text analysis in a unified retrieval framework. When an image query is provided, the system extracts visual features for similarity matching while also generating or utilizing associated semantic descriptors to maintain semantic understanding and enable more accurate retrieval.
Solution Approach 2:
The system uses deep learning models as intermediaries that bridge visual and semantic domains. These models process image inputs and generate both visual embeddings for similarity search and semantic representations that preserve meaning, allowing the system to retrieve images based on visual similarity while maintaining semantic coherence.
3Reliability
If separate search systems are maintained for text and images, then each modality can be optimized, but system complexity increases
Solution Approach 1:
The patent consolidates multiple modality-specific search systems into a single unified retrieval architecture. Instead of maintaining separate text search and image search systems, the patent implements one system that accepts multiple query types and processes them through a common embedding and retrieval pipeline, reducing overall system complexity while maintaining optimization for each modality.
Solution Approach 2:
The unified search system is designed with multi-functionality to handle text queries, image queries, and multi-modal queries through a single architecture. This universal system maintains optimized processing paths for each modality type while sharing common infrastructure, thereby reducing redundancy and simplifying system maintenance.
4Adaptability or versatility
If multi-modal queries are supported, then search versatility improves, but computational complexity increases
Solution Approach 1:
The patent segments the multi-modal query processing into distinct modular components: text processing module, image processing module, and unified retrieval module. Each module handles its specific modality independently, extracting features and generating embeddings, then passes results to the unified retrieval system. This segmentation allows parallel processing and reduces computational overhead compared to processing all modalities in a single monolithic pipeline.
Solution Approach 2:
The system performs preliminary processing of text and image queries separately before combining results for unified retrieval. Text queries are processed to generate semantic embeddings in advance, while image queries undergo feature extraction and embedding generation beforehand. These pre-computed representations are then efficiently combined and searched, reducing real-time computational complexity during actual query execution.
Data Source
AI summary
The present invention relates to methods for searching for two-dimensional or three-dimensional objects. More particularly, the present invention relates to searching for two-dimensional or three-dimensional objects in a collection by using a multi-modal query of image and/or tag data. Aspects and/or embodiments seek to provide a method of searching for digital objects using any combination of images, three-dimensional shapes and text by embedding the vector representations for these multiple modes in the same space. Aspects and/or embodiments can be easily extensible to any other type of modality, making it more general.