Multimodal Search Request Fusion Using Cross-Modal Attention
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Solution Overview
Problem
Search engines face challenges in handling multimodal information due to the diversity and complexity of data types such as text, images, and video, often requiring separate processing and failing to provide unified retrieval, leading to unsatisfactory search results.
Innovation Solution
A processing method that integrates multimodal information by converting different forms of data into feature vectors using methods like Word2Vec, CNN, and 3D convolutional neural networks, and employs a cross-modal attention model for mapping and fusion, generating a unified search request that considers the importance and correlation of different modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate processing engines are used for different data types, then each data type can be processed independently, but unified retrieval of multimodal information cannot be achieved
Solution Approach 1:
The patent merges multiple separate processing engines for different data types into a unified processing framework. The cross-modal attention model integrates text, image, and video processing capabilities into a single system that can handle multimodal information uniformly, enabling unified retrieval while maintaining the ability to process different data types independently through specialized modules.
Solution Approach 2:
The processing system is designed with universal capabilities to handle multiple data types through a common architecture. The cross-modal attention mechanism serves as a universal processing unit that can operate on different modalities (text, image, video) and generate unified search requests, making the system multi-functional without requiring separate dedicated engines for each data type.
2Ease of operation
If single-modal search results are provided, then the search process is simple, but users cannot attain search results that fully satisfy their needs
Solution Approach 1:
The patent combines results from different modalities (text, image, video) into unified search results that satisfy diverse user needs. The cross-modal attention model processes multiple input modalities and generates integrated search requests that retrieve information across different data types, providing comprehensive results that maintain simplicity while improving satisfaction.
Solution Approach 2:
The cross-modal attention model acts as an intermediary that bridges different modalities and user needs. It processes various input types (single-modal or multimodal) and generates appropriate search requests that retrieve comprehensive results, mediating between the simplicity of the search process and the complexity of satisfying diverse user requirements.
3Productivity
If multimodal information is processed separately, then processing is straightforward, but search accuracy and relevance are reduced
Solution Approach 1:
The patent performs preliminary processing of different modalities through dedicated encoders (text encoder, image encoder, video encoder) before integrating them through the cross-modal attention model. This preliminary processing maintains processing efficiency for each modality while preparing the data for accurate integration, ensuring both productivity and precision in the final search results.
Solution Approach 2:
The cross-modal attention model serves as an intermediary that accurately integrates information from different modalities. It processes the output from various encoders and generates unified search requests that capture the relationships between different data types, improving search accuracy and relevance while maintaining processing efficiency through structured integration.
Data Source
AI summary
A processing method includes obtaining input information, the input information including first information in a first form and second information in a second form, the first form being different from the second form; obtaining first process information according to the first information, and obtaining second process information according to the second information; and generating a search request corresponding to the input information according to the first process information and the first form, and the second process information and the second form.