Visual Graph Fusion for Visual Question Answering Accuracy
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Solution Overview
Problem
Current visual question answering systems face challenges in cross-modal data fusion and effective relationship reasoning, leading to reduced accuracy and increased computational overhead, particularly when dealing with complex images and long question sentences.
Innovation Solution
The method constructs a Visual Graph using Node and Edge Features, updates the Node Feature through Graph Convolutional Layers, and fuses it with question features using an attention mechanism to generate accurate answers, enhancing reasoning ability and reducing noise impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual question answering methods are used, then the system can process simple images and questions, but the accuracy decreases and computational overhead increases when dealing with complex images and long question sentences
Solution Approach 1:
The patent segments the image processing by constructing a Visual Graph that identifies and separates key objects and their relationships. The graph structure divides the complex image into discrete nodes (objects) and edges (relationships), allowing the system to focus computational resources on relevant elements rather than processing the entire image uniformly. This segmentation enables accurate handling of complex images by breaking them down into manageable components.
2Reliability
If more features and relationships are extracted to improve answer accuracy, then the reasoning capability improves, but the computational overhead increases
Solution Approach 1:
The Visual Graph serves as an intermediary structure that efficiently captures and organizes object relationships. Instead of directly computing all possible relationships between image elements, the graph acts as a mediator that pre-structures the data, allowing the attention mechanism to quickly query relevant relationships. This intermediary representation enables sophisticated reasoning without proportionally increasing computational overhead, as the graph efficiently encodes spatial and semantic relationships.
3Measurement precision
If the system processes all image features comprehensively to improve interpretability, then the answer accuracy improves, but the processing time increases
Solution Approach 1:
The patent employs a dynamic attention mechanism that adaptively adjusts which graph features to process based on the specific question. Rather than statically processing all image features, the attention mechanism dynamically weights and selects relevant nodes and edges in the Visual Graph according to the question's requirements. This dynamic processing reduces unnecessary computations for irrelevant features while maintaining comprehensive analysis where needed, thereby improving processing efficiency without sacrificing accuracy or interpretability.
Data Source
AI summary
The present disclosure provides a method for visual question answering, which relates to a field of computer vision and natural language processing. The method includes: acquiring an input image and an input question; constructing a Visual Graph based on the input image, wherein the Visual Graph comprises a Node Feature and an Edge Feature; updating the Node Feature by using the Node Feature and the Edge Feature to obtain an updated Visual Graph; determining a question feature based on the input question; fusing the updated Visual Graph and the question feature to obtain a fused feature; and generating a predicted answer for the input image and the input question based on the fused feature. The present disclosure further provides an apparatus for visual question answering, a computer device and a non-transitory computer-readable storage medium.