Image Object Recognition with Natural-Language Query Disambiguation
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Solution Overview
Problem
Existing machine learning models for object recognition from images require repetitive question-answering interactions with users due to ambiguous natural language queries, leading to inefficiencies and complexity in the recognition process.
Innovation Solution
A method and apparatus that utilize a machine learning model to recognize objects from images based on natural language queries, determining if the number of instances satisfies a predetermined condition, and if not, engaging in additional question-answering processes to refine the query, with a self-evolving training mechanism to improve accuracy and reduce interaction complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the machine learning model uses natural language queries for object recognition, then the ease of operation is improved, but the device complexity increases due to ambiguous queries requiring repetitive confirmation
Solution Approach 1:
The system performs preliminary analysis of the natural language query to determine whether it is ambiguous before initiating the recognition process. This preliminary action allows the system to prepare appropriate disambiguation strategies in advance, reducing the need for repetitive back-and-forth interactions with the user and simplifying the overall operational flow.
Solution Approach 2:
The system implements a feedback mechanism where the machine learning model provides recognition results to the user, and based on user confirmation or correction, iteratively refines the recognition process. This feedback loop resolves ambiguity efficiently by involving the user only when necessary, rather than requiring repetitive confirmation for every query.
2Measurement precision
If the machine learning model repeatedly confirms recognition targets with the user, then the measurement precision is improved, but the loss of time increases due to multiple interactions
Solution Approach 1:
The system employs self-service mechanisms where the machine learning model autonomously resolves ambiguous queries by analyzing contextual information, generating multiple candidate interpretations, and selecting the most probable one without requiring user intervention. User confirmation is requested only when the system's confidence threshold is not met, significantly reducing interaction time while maintaining precision.
Solution Approach 2:
The system dynamically adjusts the confidence threshold parameter based on the ambiguity level of the query. For highly ambiguous queries, a lower threshold allows faster automatic resolution, while for critical recognitions, a higher threshold ensures greater precision by triggering user confirmation only when absolutely necessary.
3Manufacturing precision
If the machine learning model improves understanding ability to process images accurately, then the manufacturing precision is improved, but the device complexity increases due to additional training mechanisms
Solution Approach 1:
The system merges the object recognition model with a natural language processing model into a unified architecture that jointly processes both image and query data. This integration shares computational resources and parameters between the two functions, improving recognition accuracy while avoiding the overhead of completely separate systems.
Solution Approach 2:
The machine learning model is designed with multi-functionality to handle both standard object recognition and ambiguous query disambiguation tasks using the same underlying architecture. This universal model reduces device complexity by eliminating the need for specialized components for different recognition scenarios.
Data Source
AI summary
Methods, apparatuses, devices and media for object recognition from an image are provided. In a method, a query expressed in a natural language is received, the query specifying an attribute of an object to be recognized from the image. At least one instance of the object is recognized from the image based on the query using a machine learning model. The at least one instance is provided in response to determining that a number of the at least one instance satisfies a predetermined condition. By the example implementations of the subject matter described herein, rounds of conversation between the machine learning model and a user may be significantly reduced, thereby the object is recognized from the image in a simpler and more efficient way.