A Survey on Interpretability in Visual Recognition

This paper surveys eXplainable AI (XAI, methods for making AI decisions understandable to humans) in visual recognition, which is increasingly important for safety-critical applications like autonomous driving and medical diagnostics. The survey organizes XAI approaches by intent, object, presentation, and methodology, and also examines how interpretability applies to Multimodal Large Language Models (AI systems that process and combine text, images, and other data types).