ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
ArcAD: 冷启动监督异常检测的异常校正校准框架
机构 * Faculty of Computing, Harbin Institute of Technology, Harbin, China University of New South Wales, Sydney, Australia School of Biomedical Engineering, Tsinghua University, Beijing, China School of Artificial Intelligence ; Robotics, Hunan University, Changsha, China Big data institute, Central South University, Sydney, Australia DZ-Matrix, Beijing, China Chongqing Research Institute of HIT, Chongqing, China
AI总结 针对工业异常检测中冷启动场景下正常样本不足、异常样本稀少的问题,提出基于推拉学习范式的即插即用校准框架ArcAD,通过将正常样本投影到超球面并聚类、合成伪异常并利用真实异常优化边界,在多个数据集上显著优于现有方法。
Comments Accepted to European Conference on Computer Vision (ECCV) 2026