视觉基础模型的可解释性：综述
Explainability for Vision Foundation Models: A Survey
AI总结：
本综述系统梳理视觉基础模型与可解释人工智能的交叉研究，按架构分类并探讨挑战与评估方法，为未来发展指明方向。
摘要：
随着人工智能系统日益融入日常生活，可解释性领域受到了显著关注。这一趋势尤其受到现代人工智能模型及其决策过程复杂性的驱动。基础模型的出现，以其广泛的泛化能力和新兴用途为特征，进一步使这一领域复杂化。基础模型在可解释性领域中占据着模糊的地位：其复杂性使其本质上难以解释，然而它们又越来越多地被用作构建可解释模型的工具。在本综述中，我们探讨了视觉领域中基础模型与可解释人工智能（XAI）的交集。我们首先编制了一个连接这两个领域的论文综合语料库。接下来，我们根据这些工作的架构特征对其进行分类。然后，我们讨论了当前研究在将XAI整合到基础模型中所面临的挑战。此外，我们回顾了这些组合方法的常见评估方法论。最后，我们提出了从本综述中获得的关键观察和见解，为这一快速发展的领域提供了未来研究方向。
英文摘要：
As artificial intelligence systems become increasingly integrated into daily life, the field of explainability has gained significant attention. This trend is particularly driven by the complexity of modern AI models and their decision-making processes. The advent of foundation models, characterized by their extensive generalization capabilities and emergent uses, has further complicated this landscape. Foundation models occupy an ambiguous position in the explainability domain: their complexity makes them inherently challenging to interpret, yet they are increasingly leveraged as tools to construct explainable models. In this survey, we explore the intersection of foundation models and eXplainable AI (XAI) in the vision domain. We begin by compiling a comprehensive corpus of papers that bridge these fields. Next, we categorize these works based on their architectural characteristics. We then discuss the challenges faced by current research in integrating XAI within foundation models. Furthermore, we review common evaluation methodologies for these combined approaches. Finally, we present key observations and insights from our survey, offering directions for future research in this rapidly evolving field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
