3 papers
cs.CV2026
Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection
Kaiqiang Li, Gang Li, Mingle Zhou +3
Zero-shot (ZS) 3D anomaly detection is crucial for reliable industrial inspection, as it enables detecting and localizing defects without requiring any target-category training dat…
cs.CV2026
Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
Mingle Zhou, Jiahui Liu, Jin Wan +2
Unsupervised Continuous Anomaly Detection (UCAD) is gaining attention for effectively addressing the catastrophic forgetting and heavy computational burden issues in traditional Un…
cs.CV2025
MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection
Gang Li, Tianjiao Chen, Mingle Zhou +3
Zero-shot 3D (ZS-3D) anomaly detection aims to identify defects in 3D objects without relying on labeled training data, making it especially valuable in scenarios constrained by da…