3 papers
cs.CV2026
Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models
Tai Le-Gia
Zero-shot anomaly detection (ZSAD) is attractive for medical imaging because clinical systems must handle heterogeneous acquisition protocols, changing patient populations, and pat…
cs.CV2025
On the Problem of Consistent Anomalies in Zero-Shot Anomaly Detection
Tai Le-Gia
Zero-shot anomaly classification and segmentation (AC/AS) aim to detect anomalous samples and regions without any training data, a capability increasingly crucial in industrial ins…
cs.CV2025
On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection
Tai Le-Gia, Ahn Jaehyun
Zero-shot image anomaly classification (AC) and segmentation (AS) are vital for industrial quality control, detecting defects without prior training data. Existing representation-b…