Neural-Symbolic VQA With Confidence-Guided Reasoning Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data-driven visual question answering (VQA) models lack explainability, are computationally costly, prone to biases, and fail to maintain reasoning consistency, especially in safety-critical applications, while neural-symbolic methods do not adequately address uncertainty in reasoning and user interactions.
Innovation Solution
A confidence-based neural-symbolic framework that incorporates uncertainty awareness through scene perception and question parsing, providing confidence evaluations for each inference step, allowing user interactions and feedback to improve answer reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If data-driven methods (deep learning) are used for VQA, then end-to-end processing capability is improved, but explainability deteriorates and computational cost increases
Solution Approach 1:
The system segments the VQA process into distinct modules: an AI scene perception model for image understanding, an AI question parsing model for question analysis, and a symbolic program execution module for reasoning. Each module produces intermediate results that can be independently evaluated and explained, resolving the black-box nature of end-to-end deep learning while maintaining automated processing.
2Extent of automation
If data-driven methods (deep learning) are used for VQA, then end-to-end processing capability is improved, but computational cost increases
Solution Approach 1:
By dividing the system into specialized modules (scene perception, question parsing, symbolic execution), each component can be optimized independently. The symbolic program execution module performs efficient logical operations on extracted features rather than requiring heavy deep learning inference for every reasoning step, reducing overall computational cost.
3Productivity
If data-driven VQA models are used, then processing capability is improved, but reasoning consistency deteriorates due to bias exploitation
Solution Approach 1:
The patent introduces symbolic programs as an intermediary between the AI models and the final answer. These symbolic programs represent explicit reasoning logic that can be executed deterministically, ensuring reasoning consistency. The symbolic execution module maintains logical integrity by faithfully executing the parsed question logic without the biases that plague pure data-driven approaches.
4Reliability
If neural networks are treated as black-box models in NS methods, then reasoning transparency is improved, but user interaction capability deteriorates
Solution Approach 1:
The system implements feedback mechanisms where confidence scores from the scene perception and question parsing models are used to determine when user interaction is needed. When confidence is low, the system can solicit user input to disambiguate features or clarify question intent, creating a loop that improves both transparency and usability.
Data Source
AI summary
A method of performing visual question answering (VQA), including: obtaining an image and a question corresponding to the image; generating a plurality of feature predictions about at least one object included in the image by providing the image to an artificial intelligence (AI) scene perception model; generating a plurality of symbolic programs and a plurality of program confidence scores by providing the question to an AI question parsing model; selecting a symbolic program associated with a program confidence score which is highest among the plurality of program confidence scores; executing the selected symbolic program by performing a set of logic operations included in the selected symbolic program on the plurality of feature predictions; and determining a natural language answer to the question based on a result of the set of logic operations.