CVII:通过计算机视觉解释性指数提高智能传感器系统的解释性
Hossein Mohammadi1, Krishnaprasad Thirunarayan1, Lingwei Chen1
1Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
这项研究引入了计算机视觉解释性指数 (CVII),以量化AI视觉模型的可理解性. CVII框架显示了图像解释性,模型选择和其得分之间的强烈联系.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 智能传感器系统越来越依赖于人工智能 (AI).
- 像深度神经网络 (DNN) 这样的不透明AI模型需要可解释性来实现信任和合规.
- 量化AI的解释性,特别是计算机视觉,是一个复杂的,主观的挑战.
研究的目的:
- 引入一个新的框架,计算机视觉解释性指数 (CVII),用于量化AI视觉任务中的解释性.
- 开发一个模拟人类认知过程在视觉理解的指标.
- 解决主观解释性评估中客观措施的需要.
主要方法:
- 开发了计算机视觉可解释性指数 (CVII) 框架.
- 通过使用各种计算机视觉模型评估CVII.
- 测试的模型在文本中共同对象 (COCO) 数据集,一个标准的基准.
主要成果:
- 在图像解释性和CVII分数之间建立了显著的相关性.
- 证明了CVII与选择合适的计算机视觉模型之间的联系.
- 验证了CVII在评估模型解释性方面的有效性.
结论:
- CVII框架提供了一种可量化的方法来评估和提高计算机视觉中的AI可解释性.
- 这有助于提高人工智能驱动的智能传感器系统决策的透明度和可靠性.
- CVII使利益相关者能够更好地理解和利用人工智能技术.
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