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Visualizing Visual Adaptation
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Explicitly diverse visual question generation.

Jiayuan Xie1, Jiasheng Zheng2, Wenhao Fang3

  • 1Department of Computing, Hong Kong Polytechnic University, Hong Kong SAR, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 22, 2024
PubMed
Summary

This study introduces a new model for diverse visual question generation, creating multiple interpretable questions from images. The method uses scene graphs to ensure questions are based on clear visual elements, enhancing understanding.

Keywords:
Diverse visual question generationInterpretable text generationMultimodalUnbiased scene graph generation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Automatic visual question generation has advanced, but often lacks question diversity and interpretability.
  • Existing models struggle to provide clear sources for generated questions, limiting their utility in daily tasks.

Purpose of the Study:

  • To develop a visual question generation model that explicitly produces diverse questions.
  • To ensure generated questions are based on interpretable sources within the image.
  • To improve the practical applicability of visual question generation systems.

Main Methods:

  • Image scene graphs are extracted using an unbiased scene graph generation method for interpretable question sourcing.
  • A subgraph selector is employed to learn human-like selection of diverse subgraphs for question generation.
  • The model generates diverse questions by utilizing different selected subgraphs as sources.

Main Results:

  • The proposed model successfully generates diverse questions with interpretable sources.
  • Experiments on VQA v2.0 and COCO-QA datasets demonstrate superior performance compared to baseline methods.
  • The model shows a strong ability to interpretably generate a variety of questions about an image.

Conclusions:

  • The developed model addresses the limitations of existing methods by focusing on diversity and interpretability in visual question generation.
  • Scene graph analysis and subgraph selection provide a robust framework for generating meaningful and source-traceable questions.
  • This approach enhances the utility of visual question generation for applications requiring clear question origins.