8 papers
Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection
Yihan Sun, Yuqi Cheng, Junjie Zu +5
Industrial 3D anomaly detection performance is fundamentally constrained by the scarcity and long-tailed distribution of abnormal samples. To address this challenge, we propose Syn…
VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detection
Yuxin Jiang, Yunkang Cao, Yuqi Cheng +2
Few-Shot Anomaly Detection (FSAD) has emerged as a critical paradigm for identifying irregularities using scarce normal references. While recent methods have integrated textual sem…
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…