Jianfeng Qiu — accepted AI-conference papers

2026

CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection

Ke Xu, Xinle Wang, Yanning Hou, Xueliang Ma, Juan Xie, Jianfeng Qiu

ICML 2026poster

Zero-shot 3D anomaly detection is essential for industrial quality inspection, where labeled anomaly samples are scarce. Meanwhile, existing methods lack an effective mechanism to fuse complementary 2D color images with 3D geometric structures, limiting their ability to detect both surface and struc…

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2026

VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer

Yanning Hou, Peiyuan Li, Zirui Liu, Yitong Wang, Yanran Ruan, Jianfeng Qiu +1

CVPR 2026

Zero-shot anomaly detection (ZSAD) requires detecting and localizing anomalies without access to target-class anomaly samples. Mainstream methods rely on vision-language models (VLMs) such as CLIP: they build hand-crafted or learned prompt sets for normal and abnormal semantics, then compute image-t

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