27 citations · 28 across the 3 of their papers we have counts for
3 papers
Memory-Distilled Selection for Noise-Robust Anomaly Detection
Sirojbek Safarov, Jaewoo Park, Yoon Gyo Jung +4
Anomaly detection (AD) under data contamination is critical for deploying unsupervised defect detection in industrial environments, where curating perfectly clean training sets is…
Background-Aware Defect Generation for Robust Industrial Anomaly Detection
Youngjae Cho, Gwangyeol Kim, Sirojbek Safarov +2
Detecting anomalies in industrial settings is challenging due to the scarcity of labeled anomalous data. Generative models can mitigate this issue by synthesizing realistic defect…
MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation
Ehtesham Iqbal, Sirojbek Safarov, Seongdeok Bang
Few-shot segmentation aims to segment unseen-class objects given only a handful of densely labeled samples. Prototype learning, where the support feature yields a singleor several…