52 citations · 55 across the 7 of their papers we have counts for
6 papers · 1 filter
Training-Free Anomaly Generation via Dual-Attention Enhancement in Diffusion Model
Zuo Zuo, Jiahao Dong, Yanyun Qu +1
Industrial anomaly detection (AD) plays a significant role in manufacturing where a long-standing challenge is data scarcity. A growing body of works have emerged to address insuff…
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
Mengqi Lei, Siqi Li, Yihong Wu +7
The YOLO series models reign supreme in real-time object detection due to their superior accuracy and computational efficiency. However, both the convolutional architectures of YOL…
HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection
Zuo Zuo, Jiahao Dong, Yue Gao +1
In the manufacturing industry, defect detection is an essential but challenging task aiming to detect defects generated in the process of production. Though traditional YOLO models…
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP
Zuo Zuo, Jiahao Dong, Yao Wu +2
Industrial anomaly classification (AC) is an indispensable task in industrial manufacturing, which guarantees quality and safety of various product. To address the scarcity of data…
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation
Zuo Zuo, Jiahao Dong, Yao Wu +2
Few-shot anomaly detection methods can effectively address data collecting difficulty in industrial scenarios. Compared to 2D few-shot anomaly detection (2D-FSAD), 3D few-shot anom…
Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task
Jie Liu, Yao Wu, Xiaotong Luo +1
In industrial scenarios, it is crucial not only to identify anomalous items but also to classify the type of anomaly. However, research on anomaly multi-classification remains larg…