Author

Jing Jin

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Preprint Jul 2026

MOPDA: Mixed-Trajectory On-Policy Distillation for Language-Guided Industrial Anomaly Detection

Under a strict cross-dataset zero-shot protocol on five IAD benchmarks, MOPDA outperforms the evaluated LVLM-based baselines on most detection, localization, and judgment metrics while remaining competitive with CLIP-based methods.

Shuimu Chen, Jing Jin, Nan Su et al. · 1 citation

#computer vision Jul 2026

OPD-IAD: From Language Judgment to Industrial Anomaly Detection via On-Policy Self-Distillation

Large vision-language models (LVLMs) have recently shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable defect reasoning. However, current LVLM-based IAD methods still struggle to produce precise pixel-level anomaly maps from generated language judgm...

Shuimu Chen, Jing Jin, Nan Su et al. · arXiv.org · 1 citation