5 citations · 8 across the 6 of their papers we have counts for
6 papers
URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection
Wei Luo, Peng Xing, Yunkang Cao +3
Unsupervised anomaly detection plays a pivotal role in industrial defect inspection and medical image analysis, with most methods relying on the reconstruction framework. However,…
3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly
Enquan Yang, Peng Xing, Hanyang Sun +4
Industrial anomaly detection achieves progress thanks to datasets such as MVTec-AD and VisA. However, they suffer from limitations in terms of the number of defect samples, types o…
A Recover-then-Discriminate Framework for Robust Anomaly Detection
Peng Xing, Dong Zhang, Jinhui Tang +1
Anomaly detection (AD) has been extensively studied and applied in a wide range of scenarios in the recent past. However, there are still gaps between achieved and desirable levels…
Inv-Adapter: ID Customization Generation via Image Inversion and Lightweight Adapter
Peng Xing, Ning Wang, Jianbo Ouyang +1
The remarkable advancement in text-to-image generation models significantly boosts the research in ID customization generation. However, existing personalization methods cannot sim…
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection
Peng Xing, Hao Tang, Jinhui Tang +1
Knowledge Distillation-based Anomaly Detection (KDAD) methods rely on the teacher-student paradigm to detect and segment anomalous regions by contrasting the unique features extrac…
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection
Peng Xing, Yanpeng Sun, Zechao Li
Unsupervised anomaly detection is a challenging task in industrial applications since it is impracticable to collect sufficient anomalous samples. In this paper, a novel Self-Super…