1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
Hypergraph Foundation Model
Yue Gao, Yifan Feng, Shiquan Liu +4
Hypergraph neural networks (HGNNs) effectively model complex high-order relationships in domains like protein interactions and social networks by connecting multiple vertices throu…
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…