梳理视觉大模型与可解释性技术的融合现状与挑战
Explainability for Vision Foundation Models: A Survey
- 系统整理跨领域论文，按架构分类分析
- 指出当前研究在解释性评估上缺乏统一标准
- 适合关注AI透明度与大模型安全的从业者
随着人工智能日益融入日常生活，可解释性问题受到广泛关注。这一趋势尤其由现代AI模型的复杂性及其决策过程所推动。基础模型凭借其强大的泛化能力与涌现用途，进一步加剧了该领域的复杂性。基础模型在可解释性领域处于模糊地位：其内在复杂性使其难以解释，却常被用作构建可解释模型的工具。本文综述视觉领域中基础模型与可解释AI（XAI）的交叉研究。首先，我们整合了相关领域的论文库；其次，基于模型架构对研究进行分类；接着讨论当前将XAI融入基础模型所面临的挑战；此外，回顾了常见评估方法；最后，总结关键发现并提出未来研究方向。
原文摘要 · Abstract (English)
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.
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