AD-Copilot trains an MLLM on a new curated industrial dataset Chat-AD with a Comparison Encoder that uses cross-attention on image pairs, reaching 82.3% accuracy on MMAD and 3.35x gains on MMAD-BBox while generalizing and exceeding human experts on some tasks.
Normal- abnormal guided generalist anomaly detection
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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cs.CV 2years
2026 2representative citing papers
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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AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison
AD-Copilot trains an MLLM on a new curated industrial dataset Chat-AD with a Comparison Encoder that uses cross-attention on image pairs, reaching 82.3% accuracy on MMAD and 3.35x gains on MMAD-BBox while generalizing and exceeding human experts on some tasks.
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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.