4 citations · 4 across the 3 of their papers we have counts for
4 papers
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Jingqi Wu, Hanxi Li, Lin Yuanbo Wu +3
Industrial product inspection is often performed using Anomaly Detection (AD) frameworks trained solely on non-defective samples. Although defective samples can be collected during…
Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection
Hanxi Li, Jingqi Wu, Lin Yuanbo Wu +4
In this paper, we propose Self-Navigated Residual Mamba (SNARM), a novel framework for universal industrial anomaly detection that leverages ``self-referential learning'' within te…
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Hanxi Li, Jingqi Wu, Lin Yuanbo Wu +3
In the realm of practical Anomaly Detection (AD) tasks, manual labeling of anomalous pixels proves to be a costly endeavor. Consequently, many AD methods are crafted as one-class c…
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers
Hanxi Li, Jingqi Wu, Deyin Liu +4
Recent advancements in industrial anomaly detection (AD) have demonstrated that incorporating a small number of anomalous samples during training can significantly enhance accuracy…