3 papers
cs.CV2026
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…
cs.CV2025
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…
cs.CV2025
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…