  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.
