解析视觉AI决策过程，让黑箱模型变透明
xAI-CV: An Overview of Explainable Artificial Intelligence in Computer Vision
- 四种可解释AI方法：显著图、概念瓶颈、原型法与混合模型
- 对比分析各方法机制与评估指标，揭示优劣
- 适合研究者与工程师了解AI可解释性技术路线
深度学习已成为图像分析任务的主流范式，性能达到顶尖水平。然而，这类模型常表现为“黑箱”，其决策过程难以解释，引发在关键应用中对可靠性的担忧。为解决此问题并帮助人类理解AI如何处理与决策，可解释人工智能（xAI）应运而生。本文综述了四种代表性视觉感知任务中的xAI方法：(i) 显著图，(ii) 概念瓶颈模型（CBM），(iii) 原型方法，(iv) 混合方法。分析其内在机制、优缺点及评估指标，提供全面概览，以指导未来研究与应用。
原文摘要 · Abstract (English)
Deep learning has become the de facto standard and dominant paradigm in image analysis tasks, achieving state-of-the-art performance. However, this approach often results in "black-box" models, whose decision-making processes are difficult to interpret, raising concerns about reliability in critical applications. To address this challenge and provide human a method to understand how AI model process and make decision, the field of xAI has emerged. This paper surveys four representative approaches in xAI for visual perception tasks: (i) Saliency Maps, (ii) Concept Bottleneck Models (CBM), (iii) Prototype-based methods, and (iv) Hybrid approaches. We analyze their underlying mechanisms, strengths and limitations, as well as evaluation metrics, thereby providing a comprehensive overview to guide future research and applications.
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