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cs.CV2026
DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection
Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang +2
Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the…
cs.CV2024
Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction
Po-Hsuan Huang, Chia-Ching Lin, Chih-Fan Hsu +2
Learning from noisy-labeled data is crucial for real-world applications. Traditional Noisy-Label Learning (NLL) methods categorize training data into clean and noisy sets based on…