User-Defined Object Detection via Multimodal Text Characterization
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Solution Overview
Problem
Existing object detection systems struggle to recognize user-defined categories due to limitations in vision-language multimodal models, which require substantial computational resources and strict input data, leading to slow detection speeds and limited applicability in generating appropriate text characterizations for custom detection targets.
Innovation Solution
A method and system for object detection based on user-defined categories using an auxiliary characterization generation technique, multimodal reconstruction and alignment network, and image-adaptive target characterization matching estimation to generate suitable text characterizations from user inputs, optimizing the vision-language multimodal model with feedback data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If vision-language multimodal models are used for object detection with user-defined categories, then the detection capability for custom objects is improved, but the computational resources required and detection speed deteriorate
Solution Approach 1:
The system segments the complex vision-language multimodal model into separate components: an image processing module that extracts visual features, a text processing module that handles user inputs, and a detection module. This segmentation allows each component to operate independently and efficiently, improving overall detection speed while maintaining custom object detection capability.
Solution Approach 2:
The patent introduces an intermediary text characterization generation module that translates user-defined custom objects into standardized text descriptions compatible with the vision-language model. This intermediary layer reduces the computational burden on the main model by pre-processing and structuring inputs, thereby improving detection speed without sacrificing adaptability to custom categories.
2Adaptability or versatility
If vision-language multimodal models are used for object detection with user-defined categories, then the detection capability for custom objects is improved, but the complexity of input data requirements and system configuration worsens
Solution Approach 1:
The system implements self-service through automated text characterization generation. When users input custom object names, the system automatically generates appropriate text descriptions and characterizations without requiring manual configuration or complex input formatting. This reduces the complexity of input data requirements while maintaining the ability to detect custom objects.
Solution Approach 2:
The patent creates a universal text characterization module that handles multiple types of user inputs (natural language descriptions, keywords, structured data) and converts them into a standardized format suitable for the vision-language model. This multi-functional approach simplifies input requirements by accepting various data formats while maintaining consistent processing downstream.
3Productivity
If traditional object detection algorithms are used, then the system is simple and fast, but the ability to recognize undefined object types deteriorates
Solution Approach 1:
The system performs preliminary action by pre-processing user inputs into standardized text characterizations before they reach the vision-language model. This preparation work includes generating descriptive text, extracting key features, and structuring data in advance, which enables the use of simpler, faster detection algorithms while still achieving recognition of undefined object types through the enriched input characteristics.
Data Source
AI summary
The provided is a method and system for object detection based on user-defined categories. The method includes: a user inputting a natural language description and a related image, obtaining a detection target auxiliary input using an auxiliary characterization generation technique for a detection target based on a phrase boundary point modeling technique; calling a detection target characterization generation model based on a multimodal reconstruction and alignment network to obtain a plurality of text characterizations of the detection target; generating target reverse characterizations based on an image-adaptive target characterization matching estimation technique to meet custom requirements of the detection target; and optimizing a vision-language multimodal model based on feedback data of the detection target of the user under detection, and optimizing the vision-language multimodal model based on the feedback data during usage of custom object detection.