Multimodal LLM Vision Encoder Mixtures for Fine Image Detail
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Solution Overview
Problem
Conventional multimodal large language models (MLLMs) often fail to perceive and understand smaller details in images, leading to incorrect outputs and hallucinations, particularly in tasks like optical character recognition and document analysis.
Innovation Solution
A method for training MLLMs that includes multiple vision encoders, pre-trained for different tasks and image sizes, through three stages: pre-alignment training, joint-projector training, and supervised fine-tuning, using captioning and instruction following datasets to enhance the model's ability to understand image details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MLLMs use standard vision encoders for image processing, then the model structure remains simple, but the model fails to perceive smaller details in images
Solution Approach 1:
The patent divides the vision encoder into multiple specialized sub-encoders, each responsible for processing different aspects of image data (e.g., global context, local details, text regions). This segmentation allows each sub-encoder to be optimized for its specific function, enabling the model to perceive smaller details while maintaining manageable structural complexity through modular design.
Solution Approach 2:
The patent applies different vision encoder configurations to different regions and types of image data. For example, high-resolution encoders are used for regions containing small details or text, while lower-resolution encoders handle background areas. This local quality approach enables detailed perception where needed without uniformly increasing complexity across the entire model.
2Measurement precision
If MLLMs process images at high resolution, then detail perception improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements dynamic resolution adjustment where the system automatically selects appropriate image processing resolutions based on the specific task requirements and image content. For tasks requiring fine detail analysis, high resolution is applied; for general understanding, lower resolution suffices. This dynamic approach maintains detail perception accuracy while minimizing processing time and computational resources.
Solution Approach 2:
The patent processes different regions of images at different resolutions simultaneously. High-resolution processing is applied locally to regions containing important details or text, while lower-resolution processing handles the remainder of the image. This selective resolution strategy preserves detail perception where critical while reducing overall processing time.
3Adaptability or versatility
If MLLMs use a single vision encoder, then the model structure is simple, but the model cannot handle diverse image processing tasks effectively
Solution Approach 1:
The patent segments the vision encoder into multiple specialized sub-encoders, each optimized for specific tasks such as global context understanding, local detail analysis, or text recognition. This segmentation enables the model to handle diverse image processing tasks effectively by deploying the appropriate sub-encoder for each task, while the modular architecture keeps the overall structure manageable.
Solution Approach 2:
The patent creates a universal vision encoder system where multiple sub-encoders work together to handle diverse tasks. The architecture is designed so that different combinations of sub-encoders can be activated based on task requirements, allowing a single unified system to perform multiple functions including detailed analysis, text recognition, and general image understanding.
Data Source
AI summary
The disclosed method for training multimodal models includes performing one or more operations to train a plurality of vision language models to generate a plurality of trained vision language models, where each trained vision language model included in the plurality of trained vision language models comprises a different vision encoder and a first language model, and performing one or more operations to train a multimodal model to generate a trained multimodal model, where the trained multimodal model comprises the different vision encoders and a second language model.