3 citations · 3 across the 9 of their papers we have counts for
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cs.CV2024★ 3 cited
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
Jia Guo, Shuai Lu, Weihang Zhang +3
Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images. Despite various advancements addressing t…
cs.CV2024
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment
Jia Guo, Haonan Han, Shuai Lu +2
Conventional unsupervised anomaly detection (UAD) methods build separate models for each object category. Recent studies have proposed to train a unified model for multiple classes…