Answer-Aware Visual Question Generation for Informative Questions
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Solution Overview
Problem
Current visual question generation (VQG) systems face challenges in generating informative and accurate questions about images due to non-informative hints, leading to inefficient and poor-quality training data for machine learning models.
Innovation Solution
A computer-implemented method using a Graph2Seq model with double hints (answer hint and visual hint) to generate questions, employing image and graph attention, and a KNN graph for aligned embeddings, reducing non-informative questions through cross-modal alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional VQG systems use single hint or basic alignment methods, then the system complexity is low, but the question generation accuracy and informativeness deteriorate
Solution Approach 1:
The system segments the question generation process into multiple independent modules: alignment module for cross-modal alignment, graph construction module for building KNN graphs, embedding module for generating node embeddings, and decoding module for sequence generation. Each module handles a specific aspect of the transformation from image to question, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces intermediate representations including KNN graphs that capture regional relationships, node embeddings that encode semantic information, and attention mechanisms that mediate between visual features and textual generation. These intermediaries bridge the gap between image input and question output, enhancing generation quality without requiring direct complex mapping.
2Productivity
If VQG systems generate questions without region-reference, then the generation process is fast, but the questions become non-informative and degrade training data quality
Solution Approach 1:
The system performs preliminary alignment of image regions with answer hints before question generation. The alignment module pre-processes the image by identifying and aligning relevant regions with potential answers, and the KNN graph is pre-constructed to establish regional relationships. This preliminary processing ensures that subsequent question generation is both efficient and informative, as the system already has structured region information ready.
3Measurement precision
If double hint alignment with KNN graph and attention mechanisms is applied, then question accuracy and training data quality improve, but computing power requirements increase
Solution Approach 1:
The attention mechanisms apply local quality by focusing computational resources on specific image regions and their corresponding graph nodes rather than processing the entire image uniformly. The graph attention mechanism selectively attends to relevant nodes in the KNN graph based on their relationship to the current generation step, reducing unnecessary computations while maintaining high question accuracy.
Data Source
AI summary
A computer-implemented method for visual question generation includes training an alignment module to analyze an image, an answer hint, and a visual hint with respect to the image. A k-nearest neighbors (KNN) graph is constructed by performing an aligned embedding for each region of the image. A node embedding component is generated by using a graph embedding component of the KNN graph. A visual question is generated by sequence decoding each image and graph of the image.