Scene Description Fusion Using Structured Memory and Multimodal AI
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Solution Overview
Problem
Existing scene recognition systems, particularly for autonomous systems, face challenges in handling complex environments and maintaining efficient contextual understanding due to reliance on neural networks that are not fully trained for these scenarios, leading to performance issues with low-quality or complex images and short-term analysis of unstructured information.
Innovation Solution
A system utilizing a Large Multimodal Model (LLM) with Visual Language Model (VLM) and Large Language Model (LLM) functionalities, combined with a structured memory to generate and compare scene descriptions, enhancing contextual understanding by integrating past and current observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are used for scene recognition, then specific tasks can be performed, but the system requires a great amount of trained models for variable tasks, increasing complexity and time/energy consumption
Solution Approach 1:
The patent employs a single Large Multimodal Model that can perform multiple scene recognition tasks across different domains (medical imaging, autonomous driving, robotics) without requiring separate trained models for each task. The LMM processes various input types (images, videos, text) and generates diverse outputs (descriptions, answers, classifications) using one unified model architecture, thereby achieving universal adaptability while reducing model complexity.
Solution Approach 2:
The patent introduces a structured memory component as an intermediary between the LMM and the scene recognition process. This structured memory stores and organizes scene information, allowing the LMM to efficiently access and utilize relevant context without requiring extensive retraining for different scenarios. The structured memory acts as a mediator that bridges the gap between raw inputs and task-specific outputs.
2Measurement precision
If neural networks are trained for specific tasks, then those tasks can be performed accurately, but the system fails when encountering complex environments not covered in training data
Solution Approach 1:
The patent performs preliminary actions by pre-training the LMM on diverse, large-scale datasets that encompass a wide variety of scenes and contexts before deployment. The structured memory is also pre-configured with organized scene information and relationships. This preliminary preparation enables the model to handle complex, unseen environments more effectively by leveraging pre-acquired knowledge and structured organizational frameworks.
Solution Approach 2:
The patent implements feedback mechanisms where the LMM continuously learns from its interactions with structured memory and scene data. The system refines its understanding by processing actual scene inputs, comparing predictions with structured memory information, and updating its knowledge base. This feedback loop enables the system to adapt to complex environments dynamically, improving accuracy over time without requiring extensive retraining.
3Productivity
If feed forward recognition pipeline is used, then image analysis can be performed, but contextual understanding is limited by the obtained real-world image quality
Solution Approach 1:
The structured memory serves as an intermediary that enriches the feed-forward recognition pipeline with additional contextual information. Instead of relying solely on the input image quality, the system queries structured memory for relevant scene context, object relationships, and semantic information. This mediator component bridges the gap between rapid image processing and deep contextual understanding by providing supplementary information that compensates for limited image quality.
Solution Approach 2:
The patent adds another dimension to the traditional two-dimensional image analysis by incorporating structured semantic information and contextual data from structured memory. This transforms the problem from purely spatial image processing to a multi-dimensional approach that includes semantic relationships, temporal context, and organized knowledge structures, thereby enhancing contextual understanding without sacrificing processing efficiency.
4Speed
If short-term analysis of unstructured information is performed, then quick processing is achieved, but past observations cannot be reconsidered efficiently
Solution Approach 1:
The structured memory acts as an intermediary that efficiently stores and retrieves past observations in an organized manner. When new scene data arrives, the system quickly processes it through the LMM while simultaneously querying structured memory for relevant historical context. This mediator enables rapid access to past observations without requiring slow, comprehensive reanalysis, thereby maintaining high processing speed while preserving and utilizing historical information.
Solution Approach 2:
The patent changes the organizational parameters of stored information from unstructured to structured formats. By organizing scene data, object relationships, and temporal information in structured memory with specific schemas and relationships, the system enables efficient retrieval and reconsideration of past observations. This parameter change from unstructured to structured organization maintains quick access while dramatically improving the ability to reconsider and reanalyze past observations when needed.
Data Source
AI summary
The invention is related to a system (1) for analyzing an image of a real world scene, the system (1) comprising image providing means (13) for obtaining the image of the real world scene, a large multimodal model LMM module (11) providing a large language model LLM functionality and a visual language model VLM functionality, wherein the LMM module (11) is configured to analyze the image using its VLM functionality for generating a first scene description of the received image, a structured memory (12) for storing real world information, wherein the structured memory (12) is connected to the LMM module (11) and configured to generate a second scene description of the received image based on the stored real world information and to 10 provide the description to the LMM module (11), wherein the LMM module (11) is configured to identify differences between the first scene description and the second scene description and to generate an enhanced scene description based on the identified mismatches.