Artificial Intelligence (XAI) has found numerous applications in computer vision. While image classification-based explainability techniques have garnered significant attention, their counterparts in semantic segmentation have been relatively neglected. Given the prevalent use of image segmentation, ranging from medical to industrial deployments, these techniques warrant a systematic look. In this paper, we present the first comprehensive survey on XAI in semantic image segmentation. This work focuses on techniques that were either specifically introduced for dense prediction tasks or were extended for them by modifying existing methods in classification. We analyze and categorize the literature based on application categories and domains, as well as the evaluation metrics and datasets used. We also propose a taxonomy for interpretable semantic segmentation, and discuss potential challenges and future research directions.

翻译：人工智能（XAI）在计算机视觉领域已有众多应用。尽管基于图像分类的可解释性技术已获得广泛关注，但其在语义分割中的对应方法相对受到忽视。鉴于图像分割在医疗到工业部署等场景中的广泛应用，这些技术值得进行系统性审视。本文首次对语义图像分割中的可解释人工智能（XAI）进行了全面综述。本工作聚焦于专门为密集预测任务引入的技术，或通过修改现有分类方法而扩展至这些任务的技术。我们基于应用类别与领域，以及所使用的评估指标与数据集，对文献进行了分析与分类。同时，我们提出了可解释语义分割的分类体系，并探讨了潜在挑战与未来研究方向。