5 papers
FreqAnchorAD: Language-Free Zero-Shot Anomaly Detection via Frequency-Deviation Anchoring
Jianfeng Qiu, Peiyuan Li, Juan Xie +4
Zero-shot anomaly detection (ZSAD) aims to detect anomalies and localize defective regions in unseen target domains without target training data. Recent ZSAD methods build on pretr…
CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection
Ke Xu, Xinle Wang, Yanning Hou +3
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…
VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer
Yanning Hou, Peiyuan Li, Zirui Liu +4
Zero-shot anomaly detection (ZSAD) requires detecting and localizing anomalies without access to target-class anomaly samples. Mainstream methods rely on vision-language models (VL…
Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts
Yanning Hou, Ke Xu, Junfa Li +2
Recently, the powerful generalization ability exhibited by foundation models has brought forth new solutions for zero-shot anomaly segmentation tasks. However, guiding these founda…
StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection
Yanning Hou, Yanran Ruan, Junfa Li +3
Enhancing the alignment between text and image features in the CLIP model is a critical challenge in zero-shot industrial anomaly detection tasks. Recent studies predominantly util…