Summary of A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect, by Yunkang Cao et al.
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
by Yunkang Cao, Xiaohao Xu, Jiangning Zhang, Yuqi Cheng, Xiaonan Huang, Guansong Pang, Weiming Shen
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive survey on Visual Anomaly Detection (VAD), which aims to identify deviations from normality in visual data. The authors highlight three primary challenges faced by recent advancements in VAD: scarcity of training data, diversity of visual modalities, and complexity of hierarchical anomalies. They categorize and discuss the latest VAD progress based on sample number, data modality, and anomaly hierarchy. The paper concludes with future developments for VAD and summarizes key findings and contributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VAD is a way to find unusual things in pictures or videos that don’t look like normal things. It’s used in many areas, such as checking for defects on machines and finding medical problems. This report looks at what people have been doing lately to improve VAD. There are three main problems: not having enough training data, dealing with different types of visual information, and trying to find complex patterns. The report shows how different approaches have been used to solve these problems and what the future might hold for VAD. |
Keywords
» Artificial intelligence » Anomaly detection
