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Found 5 papers, 2 papers with code
Produce Once, Utilize Twice for Anomaly Detection
Equipped with the above modules, POUTA is endowed with the ability to provide a more precise anomaly location than the prior arts.
Ranked #16 on
Anomaly Detection
on MVTec AD
Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation
In industrial defect segmentation tasks, while pixel accuracy and Intersection over Union (IoU) are commonly employed metrics to assess segmentation performance, the output consistency (also referred to equivalence) of the model is often overlooked.
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection
One-class classification (OCC) is a longstanding method for anomaly detection.
The Second-place Solution for CVPR VISION 23 Challenge Track 1 -- Data Effificient Defect Detection
The Vision Challenge Track 1 for Data-Effificient Defect Detection requires competitors to instance segment 14 industrial inspection datasets in a data-defificient setting.
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision
Although existing image anomaly detection methods yield impressive results, they are mostly an offline learning paradigm that requires excessive data pre-collection, limiting their adaptability in industrial scenarios with online streaming data.
