420 citations · 499 across the 14 of their papers we have counts for
14 papers
Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection
Hanzhe Liang, Guoyang Xie, Chengbin Hou +3
3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typi…
Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning
Hongze Zhu, Guoyang Xie, Chengbin Hou +4
High-resolution point clouds~(HRPCD) anomaly detection~(AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods th…
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
Jiaqi Liu, Guoyang Xie, Ruitao Chen +5
High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advanc…
EasyNet: An Easy Network for 3D Industrial Anomaly Detection
Ruitao Chen, Guoyang Xie, Jiaqi Liu +4
3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but most of them cannot…
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
Guoyang Xie, Jinbao Wang, Yawen Huang +4
The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural feature…
What makes a good data augmentation for few-shot unsupervised image anomaly detection?
Lingrui Zhang, Shuheng Zhang, Guoyang Xie +5
Data augmentation is a promising technique for unsupervised anomaly detection in industrial applications, where the availability of positive samples is often limited due to factors…
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
Guoyang Xie, Jinbao Wang, Jiaqi Liu +5
Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing (IM). Recently, many advanced algorithms have been reported, but their perfo…
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore
Guoyang Xie, Jinbao Wang, Jiaqi Liu +2
In the area of fewshot anomaly detection (FSAD), efficient visual feature plays an essential role in memory bank M-based methods. However, these methods do not account for the rela…
Deep Industrial Image Anomaly Detection: A Survey
Jiaqi Liu, Guoyang Xie, Jinbao Wang +4
The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-ba…
A Survey of Visual Sensory Anomaly Detection
Xi Jiang, Guoyang Xie, Jinbao Wang +4
Visual sensory anomaly detection (AD) is an essential problem in computer vision, which is gaining momentum recently thanks to the development of AI for good. Compared with semanti…
Cross-Modality Neuroimage Synthesis: A Survey
Guoyang Xie, Yawen Huang, Jinbao Wang +4
Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues with anatomical properties. The existence of completely aligned and paired multi-modali…
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
Jinbao Wang, Guoyang Xie, Yawen Huang +3
The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in the diagnosis of brain diseases. However, collecting the full set of we…
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis
Jinbao Wang, Guoyang Xie, Yawen Huang +4
Utilizing multi-modal neuroimaging data has been proved to be effective to investigate human cognitive activities and certain pathologies. However, it is not practical to obtain th…
Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search
Guoyang Xie, Jinbao Wang, Guo Yu +2
Due to limited computational cost and energy consumption, most neural network models deployed in mobile devices are tiny. However, tiny neural networks are commonly very vulnerable…