Cross-dataset testing of nearest-neighbor and Mahalanobis anomaly detectors on CLIP, DINOv2, ResNet-50 and EfficientNet embeddings shows same-dataset AUC averaging 0.704 dropping to 0.499 on other datasets, with false-alarm rates around 31,931 per hour at usable operating points.
org/abs/2011.08785
3 Pith papers cite this work. Polarity classification is still indexing.
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Hypergraph model on DINOv2 tokens raises logical anomaly AUROC to 0.9279 on MVTec LOCO breakfast-box data by scoring an information quotient across local, relational, and hyperedge evidence.
HLGFA detects anomalies by identifying breakdowns in cross-resolution feature consistency between high- and low-resolution views of normal samples, guided by structure and detail priors, and reports 97.9% pixel AUROC on MVTec AD.
citing papers explorer
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Benchmark AUC Is Not Deployable Reliability: A Cross-Dataset Audit of Off-the-Shelf Features for Surveillance Video Anomaly Detection
Cross-dataset testing of nearest-neighbor and Mahalanobis anomaly detectors on CLIP, DINOv2, ResNet-50 and EfficientNet embeddings shows same-dataset AUC averaging 0.704 dropping to 0.499 on other datasets, with false-alarm rates around 31,931 per hour at usable operating points.
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Hypergraph Normal World Models for Logical Visual Anomaly Detection
Hypergraph model on DINOv2 tokens raises logical anomaly AUROC to 0.9279 on MVTec LOCO breakfast-box data by scoring an information quotient across local, relational, and hyperedge evidence.
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HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection
HLGFA detects anomalies by identifying breakdowns in cross-resolution feature consistency between high- and low-resolution views of normal samples, guided by structure and detail priors, and reports 97.9% pixel AUROC on MVTec AD.