A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
It provides a comprehensive overview for researchers and practitioners in fields like industrial inspection and medical imaging, but is incremental as it synthesizes existing work rather than introducing new methods.
This survey examines recent advancements in Visual Anomaly Detection (VAD), identifying key challenges such as data scarcity and anomaly complexity, and categorizes progress based on sample number, data modality, and anomaly hierarchy.
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.