128 citations · 178 across the 22 of their papers we have counts for
9 papers · 1 filter
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…
Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation
Yuxin Jiang, Wei Luo, Hui Zhang +4
We propose Anomagic, a zero-shot anomaly generation method that produces semantically coherent anomalies without requiring any exemplar anomalies. By unifying both visual and textu…
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…
INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning
Wei Luo, Haiming Yao, Yunkang Cao +4
Anomaly detection (AD) is essential for industrial inspection and medical diagnosis, yet existing methods typically rely on ``comparing'' test images to normal references from a tr…
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…