17 papers
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
Ningning Han, Lei Fan, Jia Guo +5
The deployment of Industrial Anomaly Detection (IAD) in real-world manufacturing frequently encounters a challenging cold-start bottleneck, in which limited normal samples fail to…
Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection
Yihan Sun, Yuqi Cheng, Junjie Zu +5
Industrial 3D anomaly detection performance is fundamentally constrained by the scarcity and long-tailed distribution of abnormal samples. To address this challenge, we propose Syn…
URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection
Wei Luo, Peng Xing, Yunkang Cao +3
Unsupervised anomaly detection plays a pivotal role in industrial defect inspection and medical image analysis, with most methods relying on the reconstruction framework. However,…
VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detection
Yuxin Jiang, Yunkang Cao, Yuqi Cheng +2
Few-Shot Anomaly Detection (FSAD) has emerged as a critical paradigm for identifying irregularities using scarce normal references. While recent methods have integrated textual sem…
AnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRI
Changwei Wu, Yifei Chen, Yuxin Du +7
Reliable anomaly detection in brain MRI remains challenging due to the scarcity of annotated abnormal cases and the frequent absence of key imaging modalities in real clinical work…
Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection
Yuxin Jiang, Yunkang Cao, Weiming Shen
Few-shot anomaly detection (FSAD) denotes the identification of anomalies within a target category with a limited number of normal samples. Existing FSAD methods largely rely on pr…