3 citations · 3 across the 6 of their papers we have counts for
6 papers
Few-shot Defect Image Generation based on Consistency Modeling
Qingfeng Shi, Jing Wei, Fei Shen +1
Image generation can solve insufficient labeled data issues in defect detection. Most defect generation methods are only trained on a single product without considering the consist…
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation
Zhen Qu, Xian Tao, Mukesh Prasad +4
Recently, large-scale vision-language models such as CLIP have demonstrated immense potential in zero-shot anomaly segmentation (ZSAS) task, utilizing a unified model to directly d…
Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation
Zhen Qu, Xian Tao, Fei Shen +2
In industrial defect segmentation tasks, while pixel accuracy and Intersection over Union (IoU) are commonly employed metrics to assess segmentation performance, the output consist…
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection
Han Gao, Huiyuan Luo, Fei Shen +1
One-class classification (OCC) is a longstanding method for anomaly detection. With the powerful representation capability of the pre-trained backbone, OCC methods have witnessed s…
The Second-place Solution for CVPR VISION 23 Challenge Track 1 -- Data Effificient Defect Detection
Xian Tao, Zhen Qu, Hengliang Luo +6
The Vision Challenge Track 1 for Data-Effificient Defect Detection requires competitors to instance segment 14 industrial inspection datasets in a data-defificient setting. This re…
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision
Han Gao, Huiyuan Luo, Fei Shen +1
Although existing image anomaly detection methods yield impressive results, they are mostly an offline learning paradigm that requires excessive data pre-collection, limiting their…