ASBench is the first dedicated benchmark for anomaly synthesis algorithms, assessing them on generalization across datasets, synthetic-to-real data ratios, metric correlations, and hybrid strategies.
Ader: A comprehensive benchmark for multi-class visual anomaly detection
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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.
MambaADv2 evolves Mamba state space models with hybrid blocks, frequency convolutions, and adaptive scanning for improved unsupervised anomaly detection.
citing papers explorer
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ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection
ASBench is the first dedicated benchmark for anomaly synthesis algorithms, assessing them on generalization across datasets, synthetic-to-real data ratios, metric correlations, and hybrid strategies.
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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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MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection
MambaADv2 evolves Mamba state space models with hybrid blocks, frequency convolutions, and adaptive scanning for improved unsupervised anomaly detection.