视觉可解释的人工智能用于基于图形的视觉问题答案和场景图表策划
Sebastian Künzel1, Tanja Munz-Körner2, Pascal Tilli3
1VISUS, University of Stuttgart, Stuttgart, 70569, Germany. sebastian.kuenzel@visus.uni-stuttgart.de.
Visual computing for industry, biomedicine, and art
|April 7, 2025
概括
本研究介绍了一种新的可解释的人工智能 (XAI) 可视化工具,用于基于图形的视觉问答 (VQA) 系统. 该工具有助于识别和纠正模型错误,改善数据集质量和理解GNN决策.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 数据可视化 数据可视化
背景情况:
- 基于图形的视觉问答 (VQA) 系统往往缺乏决策过程中的透明度.
- 识别和纠正VQA模型中的错误对于提高性能和数据质量至关重要.
研究的目的:
- 在基于图形的VQA中开发一种用于可解释AI (XAI) 的新型可视化方法.
- 为了使用户能够识别错误的预测,并直接纠正输入空间中的模型错误.
- 为了促进数据集的策划和增强对图形神经网络 (GNN) 内部状态的理解.
主要方法:
- 该研究提出了一个可视化工具,与GraphVQA框架集成.
- 该系统使用图形神经网络 (GNN) 进行VQA任务,在GQA数据集上进行训练.
- 该方法突出显示了GNN内部状态,以解释模型预测.
主要成果:
- 开发的工具有效地支持用户识别错误的预测和诊断潜在问题.
- 一项与领域专家的用户研究验证了该工具的实用性和有效性.
- 量化测量和使用案例演示证实了该系统的功能.
结论:
- 新的可视化方法显著提高了基于图形的VQA系统的可解释性.
- 该工具通过允许直接纠正错误来促进数据集策划.
- 该方法可扩展到其他基于图形的问答模型.
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