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.
@article{arxiv.2401.16402,
title = {A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect},
author = {Yunkang Cao and Xiaohao Xu and Jiangning Zhang and Yuqi Cheng and Xiaonan Huang and Guansong Pang and Weiming Shen},
journal= {arXiv preprint arXiv:2401.16402},
year = {2024}
}
Comments
Work in progress. Yunkang Cao, Xiaohao Xu, and Jiangning Zhang contribute equally to this work