128 citations · 139 across the 12 of their papers we have counts for
6 papers · 1 filter
Leveraging Learning Bias for Noisy Anomaly Detection
Yuxin Zhang, Yunkang Cao, Yuqi Cheng +2
This paper addresses the challenge of fully unsupervised image anomaly detection (FUIAD), where training data may contain unlabeled anomalies. Conventional methods assume anomaly-f…
Multi-View Reconstruction with Global Context for 3D Anomaly Detection
Yihan Sun, Yuqi Cheng, Yunkang Cao +2
3D anomaly detection is critical in industrial quality inspection. While existing methods achieve notable progress, their performance degrades in high-precision 3D anomaly detectio…
A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects
Yuqi Cheng, Yunkang Cao, Haiming Yao +4
Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensi…
Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects
Yuqi Cheng, Yihan Sun, Hui Zhang +2
In industrial point cloud analysis, detecting subtle anomalies demands high-resolution spatial data, yet prevailing benchmarks emphasize low-resolution inputs. To address this disp…
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark
Yunkang Cao, Yuqi Cheng, Xiaohao Xu +6
The practical deployment of Visual Anomaly Detection (VAD) systems is hindered by their sensitivity to real-world imaging variations, particularly the complex interplay between vie…
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
Wei Luo, Yunkang Cao, Haiming Yao +5
Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, v…