一项关于视觉识别可解释性的调查
概括
本调查系统地审查可解释AI (XAI) 的视觉识别模型,这对于在关键领域的安全部署至关重要. 它对XAI方法进行了分类并评估了它们的有效性,为未来的AI可解释性研究提供了路线图.
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
背景情况:
- 视觉识别模型非常出色,但对于安全关键的应用需要了解.
- 可解释的人工智能 (XAI) 对于信任和部署在自动驾驶和医学诊断等领域至关重要.
- 视觉识别XAI独特地整合了视觉和语言,这是多式联络智能的关键.
研究的目的:
- 在视觉识别中系统地调查和分类XAI.
- 为XAI方法建立以人为中心的多维分类学.
- 探索多式大型语言模型 (MLLMs) 的可解释性及其应用.
主要方法:
- 建立了基于意图,对象,呈现和方法的分类学.
- 总结了XAI的评估需求和指标.
- 在XAI类别中进行了定性评估和定量基准.
主要成果:
- 在视觉识别中对XAI技术进行全面的分类.
- 对XAI方法与关键的需求数据和指标进行评估.
- 了解MLLM的解释性及其实际用途.
结论:
- 该调查提供了XAI在视觉识别方面的结构化概述和路线图.
- 识别了人工智能可解释性的新兴趋势和机会.
- 强调以人为中心的方法对于有效的XAI的重要性.
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