AI RESEARCH
VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer
arXiv CS.CV
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ArXi:2603.07952v1 Announce Type: new 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-text similarities for open-set discrimination. While effective, this paradigm depends on a text encoder and cross-modal alignment, which can lead to