Baoqiang Li — accepted AI-conference papers

2025

HyperTrans: Efficient Hypergraph-Driven Cross-Domain Pattern Transfer in Image Anomaly Detection

Tengyu Zhang, Deyu Zeng, Baoqiang Li, Wei Wang, Wei Liu, Zongze Wu

IJCAI 2025

Anomaly detection plays a pivotal role in industrial quality assurance processes, with cross-domain problems, exemplified by the model upgrade from RGB to 3D, being prevalent in real-world scenarios yet remaining systematically underexplored. To address the severe challenges posed by the extreme lac

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2024

CLIP-FSAC: Boosting CLIP for Few-Shot Anomaly Classification with Synthetic Anomalies

Zuo Zuo, Yao Wu, Baoqiang Li, Jiahao Dong, You Zhou, Lei Zhou +2

IJCAI 2024poster

Few-shot anomaly classification (FSAC) is a vital task in manufacturing industry. Recent methods focus on utilizing CLIP in zero/few normal shot anomaly detection instead of custom models. However, there is a lack of specific text prompts in anomaly classification and most of them ignore the modalit…

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