104 citations · 116 across the 6 of their papers we have counts for
6 papers
A Masked Reverse Knowledge Distillation Method Incorporating Global and Local Information for Image Anomaly Detection
Yuxin Jiang, Yunkang Can, Weiming Shen
Knowledge distillation is an effective image anomaly detection and localization scheme. However, a major drawback of this scheme is its tendency to overly generalize, primarily due…
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection
Yunkang Cao, Xiaohao Xu, Chen Sun +3
This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal…
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
Yunkang Cao, Xiaohao Xu, Weiming Shen
Point cloud (PCD) anomaly detection steadily emerges as a promising research area. This study aims to improve PCD anomaly detection performance by combining handcrafted PCD descrip…
SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data
Peng Peng, Hanrong Zhang, Mengxuan Li +3
Intelligent fault diagnosis has made extraordinary advancements currently. Nonetheless, few works tackle class-incremental learning for fault diagnosis under limited fault data, i.…
An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture
Wenbin Song, Di Wu, Weiming Shen +1
Early fault detection (EFD) of rotating machines is important to decrease the maintenance cost and improve the mechanical system stability. One of the key points of EFD is developi…
Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection
Wenbin Song, Di Wu, Weiming Shen +1
Early fault detection (EFD) of rolling bearings can recognize slight deviation of the health states and contribute to the stability of mechanical systems. In practice, very limited…