# Explainability for Vision Foundation Models: A Survey

arXiv: https://arxiv.org/abs/2501.12203 · HTML: https://mlsanity.com/paper/2501.12203/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [A Survey on Interpretability in Visual Recognition](https://mlsanity.com/paper/2507.11099/) (2025)
- [Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs](https://mlsanity.com/paper/2507.05810/) (2025)
- [Decomposition-Enhanced Training for Post-Hoc Attributions In Language Models](https://mlsanity.com/paper/2510.25766/) (2025)

## Abstract (arXiv)

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.