Object Detection With Selective LLM Context Integration
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Solution Overview
Problem
Existing object detection techniques rely solely on visual information and lack the ability to utilize additional information, limiting their effectiveness.
Innovation Solution
An object detection method and apparatus that integrates expression information from a generalization intelligence model like a large language model (LLM) to enhance detection capabilities, allowing selective use of additional information when available and defaulting to visual information when it is not.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object detection uses only visual information from images, then the system structure remains simple and easy to operate, but the detection performance and available information are limited
Solution Approach 1:
The patent merges visual information from images with additional information extracted by LLMs into a unified object detection system. The detection engine integrates both image features and LLM-generated attributes (such as object characteristics, relationships, and contextual information) to perform detection, thereby improving reliability while managing complexity through structured integration.
Solution Approach 2:
The object detection system is designed with multi-functionality to handle both cases where additional information is available and where it is not. The system can selectively use LLM-extracted information when available and fall back to visual-only detection when additional information is missing, making the system universally applicable across different scenarios without requiring separate specialized systems.
2Adaptability or versatility
If object detection integrates additional information from LLMs, then detection capabilities and information availability are enhanced, but the system complexity increases
Solution Approach 1:
The system employs dynamic information integration where the object detection engine can adaptively select whether to incorporate LLM-extracted additional information based on availability and relevance. The system dynamically adjusts its behavior by using a random probability mechanism to determine whether to reflect additional information or use zero vectors, enabling flexible adaptation without fixed complex architecture.
Solution Approach 2:
The patent introduces an intermediary mechanism (the object detection engine with projection modules) that mediates between the LLM extraction component and the detection output. This intermediary structure processes and transforms LLM-generated information into a format compatible with visual detection systems, reducing direct integration complexity while maintaining enhanced detection capabilities.
3Ease of operation
If the system uses zero vectors to indicate non-use of additional information, then the system can operate with minimal information, but information loss occurs when additional information is unavailable
Solution Approach 1:
The system changes the parameter representation by using zero vectors as a special case to indicate unavailability of additional information. This parameter change allows the system to maintain a unified processing framework where both available and unavailable information cases are handled through the same mechanism, simplifying operation while explicitly marking information loss scenarios for appropriate handling.
Data Source
AI summary
There are provided a method and an apparatus for object detection. An object detection method according to an embodiment includes: acquiring, by an object detection system, image information including correct answer information on objects, and additional information on objects which is extracted through a generalization intelligence model; training, by the object detection system, an object detection engine based on the acquired information; and performing, by the object detection system, object detection by using the trained object detection engine, and performing the object detection includes selectively determining whether to reflect the additional information in the process of performing the object detection according to whether the additional information on the objects is acquired.