Multi-modal Machine Learning Integrating Computer Vision and Language Models
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Solution Overview
Problem
Single-modal language models are limited in generating outputs as they only process textual content, failing to consider additional information from visual content, which can provide a more complete understanding of a topic, leading to incomplete or missing details in their responses.
Innovation Solution
Integration of a computer vision system with a language model to jointly process textual and visual content, enabling the generation of more accurate, comprehensive, and personalized outputs by utilizing visual information extracted from images and videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If single-modal language models process only textual content, then system complexity is reduced and ease of operation is improved, but information completeness and output accuracy deteriorate due to inability to consider visual content
Solution Approach 1:
The patent merges a computer vision system and a language model into a unified multi-modal system. The computer vision system extracts features from visual content while the language model processes textual content, and both are integrated to generate comprehensive outputs that incorporate both visual and textual information, thereby reducing information loss without excessive complexity increase
Solution Approach 2:
The multi-modal system is designed to handle multiple types of input (visual and textual) and perform multiple functions (feature extraction, information integration, and output generation). This universal approach allows the system to process diverse content types through a single integrated architecture, improving information completeness while maintaining operational efficiency
2Measurement precision
If single-modal language models process only textual content, then processing speed is improved and productivity is enhanced, but output accuracy and comprehensiveness deteriorate due to limited information sources
Solution Approach 1:
The computer vision system performs preliminary feature extraction from visual content before the language model generates its output. This preliminary processing of visual information allows the language model to incorporate pre-extracted visual features into its generation process, improving output accuracy without significantly impacting processing speed
Solution Approach 2:
The patent introduces an intermediary mechanism that bridges the computer vision system and the language model. This intermediary facilitates efficient information transfer and integration between the visual feature extraction component and the textual processing component, enabling accurate multi-modal output generation while maintaining processing efficiency
Data Source
AI summary
Improved multi-modal machine learning networks integrate computer vision systems with language models. In certain embodiments, a computer vision system analyzes at least one image to generate a computer vision output. The language model generates an output based, at least in part, on a consideration of the computer vision output. The outputs of the language model can be generated by jointly considering textual information learned by the language model and visual content extracted by the computer vision system, thereby significantly improving the accuracy, breadth, and comprehensiveness of the outputs.