使用变压器解码器绘制注意力地图的视觉可解释性方法用于图像标题
Meena Kowshalya1, Suchitra2, Rajesh Kumar Dhanaraj3
1Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India.
MethodsX
|December 24, 2025
概括
本研究介绍了一种可解释的图像标题框架,使用卷积神经网络编码器和变压器解码器. 它提高了人工智能决策的透明度,以确保可靠的应用.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 图像标题模型经常充当黑子,在决策过程中缺乏透明度.
- 在视觉语言模型中需要可解释的AI (XAI),以建立信任和可靠性.
研究的目的:
- 开发一个新的可解释的图像标题框架,整合视觉可解释性.
- 解决当前视觉语言模型中的透明度差距.
主要方法:
- 使用一个卷积神经网络 (CNN) 编码器和一个变压器解码器架构.
- 集成的基于注意力的热图,为生成的标题提供视觉解释.
- 在MS COCO数据集上使用标准指标 (BLEU,METEOR,CIDER,SPICE) 评估性能和可解释性.
主要成果:
- 拟议的框架为图片标题的决策过程提供了透明度.
- 基于注意力的热图有效地突出了影响标题生成的视觉特征.
- 该方法平衡了标题质量与增强的可解释性.
结论:
- 开发的框架提高了人工智能系统的可信度和透明度.
- 这种方法适用于医疗保健,教育,安全和预测等关键应用.
- 它通过在视觉语言模型中弥合性能和可解释性来促进可解释AI的进步.
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