Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspection, and medical lesion detection. This survey comprehensively examines recent advancements in VAD by identifying three primary challenges: 1) scarcity of training data, 2) diversity of visual modalities, and 3) complexity of hierarchical anomalies. Starting with a brief overview of the VAD background and its generic concept definitions, we progressively categorize, emphasize, and discuss the latest VAD progress from the perspective of sample number, data modality, and anomaly hierarchy. Through an in-depth analysis of the VAD field, we finally summarize future developments for VAD and conclude the key findings and contributions of this survey.

翻译：视觉异常检测旨在精准识别视觉数据中偏离正常概念的异常现象，广泛应用于工业缺陷检测、医学病灶发现等领域。本综述通过剖析三大核心挑战全面梳理了视觉异常检测领域的最新进展：1）训练数据稀缺性，2）视觉模态多样性，3）层次化异常的复杂性。我们从该领域的背景概述与通用概念定义出发，逐步从样本数量、数据模态和异常层级三个维度，系统地对最新研究成果进行分类、重点阐述与深入探讨。基于对该领域的深度解析，我们最终总结了视觉异常检测的未来发展方向，并凝练出本综述的关键发现与学术贡献。