Multimodal Query-Answer Model with Cross-Attention Streams
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current natural language processing technologies rely on unimodal understanding of digital documents, limiting responses to a single modality such as text or images, which misses useful information from other modalities and results in inaccurate multimodal answers when combining multiple unimodal models.
Innovation Solution
A multimodal query-answer model is developed, incorporating both textual and visual streams of transformer-based models with cross-attention layers to process and integrate multiple modalities within digital documents, generating comprehensive and accurate responses to queries by evaluating relevance across different content types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple unimodal models are combined to provide multimodal answers, then the coverage of different modalities is improved, but the accuracy of the generated answers deteriorates due to lack of understanding of relationships between modalities
Solution Approach 1:
The patent merges multiple unimodal models (textual and visual streams) into a single integrated multimodal model. The cross-attention layers enable the model to process and understand relationships between different modalities simultaneously, rather than combining separate unimodal models. This integration allows the system to maintain multimodal coverage while improving answer accuracy through unified understanding of modality relationships.
Solution Approach 2:
The cross-attention layers serve as intermediary mechanisms that facilitate interaction between different modalities. These layers enable the textual and visual streams to exchange information and understand relationships between modalities, acting as a mediator that allows the system to generate accurate multimodal answers without requiring complex coordination between separate unimodal models.
2Device complexity
If a unimodal model is used to process documents, then the model complexity is reduced, but the information completeness deteriorates by missing useful information from other modalities
Solution Approach 1:
The patent segments the multimodal model into distinct textual and visual streams, each processing its respective modality independently. Within each stream, transformer-based models handle the specific modality tasks. This segmentation allows the system to manage complexity through modular architecture while achieving information completeness by integrating results from both streams through cross-attention mechanisms.
Solution Approach 2:
The multimodal model achieves universality by designing a unified architecture that can process both textual and visual modalities through the same transformer-based framework with cross-attention layers. This multi-functional design allows the system to handle diverse content types (text, images, charts, audio, video) within a single model structure, reducing overall system complexity compared to using separate specialized models for each modality.
Data Source
AI summary
In some embodiments, a multimodal computing system receives a query and identifies, from source documents, text passages and images that are relevant to the query. The multimodal computing system accesses a multimodal question-answering model that includes a textual stream of language models and a visual stream of language models. Each of the textual stream and the visual stream contains a set of transformer-based models and each transformer-based model includes a cross-attention layer using data generated by both the textual stream and visual stream of language models as an input. The multimodal computing system identifies text relevant to the query by applying the textual stream to the text passages and computes, using the visual stream, relevance scores of the images to the query, respectively. The multimodal computing system further generates a response to the query by including the text and/or an image according to the relevance scores.