Deep visual models have widespread applications in high-stake domains. Hence, their black-box nature is currently attracting a large interest of the research community. We present the first survey in Explainable AI that focuses on the methods and metrics for interpreting deep visual models. Covering the landmark contributions along the state-of-the-art, we not only provide a taxonomic organization of the existing techniques, but also excavate a range of evaluation metrics and collate them as measures of different properties of model explanations. Along the insightful discussion on the current trends, we also discuss the challenges and future avenues for this research direction.

翻译：深度视觉模型在高风险领域有着广泛应用。因此，其黑箱特性目前正引起研究界的广泛关注。我们提出了首个聚焦于深度视觉模型解释方法与评估指标的可解释人工智能综述。本文涵盖从里程碑式贡献到前沿成果，不仅对现有技术进行了分类组织，还深入挖掘了一系列评估指标，并将其归纳为模型解释不同属性的度量标准。在就当前趋势展开富有洞见的讨论的同时，我们还探讨了这一研究方向所面临的挑战与未来前景。