Transformer-Based Object Recognition for Mixed Question Types
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Solution Overview
Problem
Existing object recognition technologies face challenges in efficiently and accurately processing diverse types of text questions, such as calculation-based and knowledge-based questions, particularly in paper quizzes, leading to inefficiencies and inaccuracies in recognition and correction processes.
Innovation Solution
An object recognition processing method utilizing a transformer learning model that applies different processing rules based on the type of question, including transformation and recognition steps to adapt to various question types, such as fill-in-the-blank, true-false, and multiple-choice questions, using a neural network-based approach to enhance recognition accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single unified recognition model is used for all question types, then the device complexity is reduced, but the recognition precision deteriorates for specific question types like fill-in-the-blank, true-false, and multiple-choice questions
Solution Approach 1:
The patent divides the question recognition system into multiple specialized sub-models, each dedicated to a specific question type (fill-in-the-blank, true-false, multiple-choice). This segmentation allows each sub-model to be optimized for its specific task, improving recognition precision while maintaining manageable complexity through modular architecture.
Solution Approach 2:
Different processing strategies and attention mechanisms are applied to different question types. For example, fill-in-the-blank questions use specific token masking strategies, while multiple-choice questions use option-specific attention weights. This local optimization of processing quality for each question type enhances overall recognition accuracy.
2Measurement precision
If different processing rules are applied for different question types, then the recognition precision is improved, but the device complexity increases due to multiple processing paths
Solution Approach 1:
The patent employs a universal transformer-based architecture that can handle multiple question types through configurable processing rules. The same base model structure serves multiple functions by dynamically adjusting processing parameters and attention mechanisms based on the detected question type, avoiding the need for completely separate processing pipelines.
Solution Approach 2:
The system performs preliminary question type classification before applying specific processing rules. This preliminary action enables the system to select the appropriate processing strategy in advance, organizing the complexity of multiple processing rules into a structured sequence of operations that is easier to manage and execute.
3Measurement precision
If transformation processing is performed for non-basic question types, then the recognition accuracy is improved, but the computational load increases
Solution Approach 1:
The patent transforms non-basic question types by modifying input parameters and token representations rather than performing complex structural transformations. For example, converting multiple-choice questions into a standardized format with option tokens allows the same base processing logic to handle them efficiently, reducing computational overhead while maintaining accuracy.
Solution Approach 2:
The system extracts and processes only the essential components of each question type, separating critical information from redundant elements. For instance, in multiple-choice questions, the system focuses on the relationship between the question stem and correct option while efficiently filtering out distractor options, reducing unnecessary computational operations.
Data Source
AI summary
An object recognition processing method, a processing apparatus, an electronic device, and a non-transitory computer-readable storage medium. The method includes: obtaining an object to be recognized; recognizing a type of the object on the basis of a type recognition model; determining a processing rule corresponding to the object; in response to the fact that the type of the object is a basic type, taking the object as a target object to be recognized, and in response to the fact that the type of the object is a non-basic type, transforming the object by means of a transformer learning model, so as to transform the object into the target object to be recognized; and performing, by means of the transformer learning model, recognition processing on the target object to be recognized to obtain a target result corresponding to the target object to be recognized.