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Reconstruction method based on the memory module for visual anomaly detectionattempts to narrow the reconstruction error for normal samples while enlargingit for anomalous samples. Unfortunately, the existing memory module is notfully applicable to the anomaly detection task, and the reconstruction error ofthe anomaly samples remains small. Towards this end, this work proposes a newunsupervised visual anomaly detection method to jointly learn effective normalfeatures and eliminate unfavorable reconstruction errors. Specifically, a novelPartition Memory Bank (PMB) module is proposed to effectively learn and storedetailed features with semantic integrity of normal samples. It develops a newpartition mechanism and a unique query generation method to preserve thecontext information and then improves the learning ability of the memorymodule. The proposed PMB and the skip connection are alternatively explored tomake the reconstruction of abnormal samples worse. To obtain more preciseanomaly localization results and solve the problem of cumulative reconstructionerror, a novel Histogram Error Estimation module is proposed to adaptivelyeliminate the unfavorable errors by the histogram of the difference image. Itimproves the anomaly localization performance without increasing the cost. Toevaluate the effectiveness of the proposed method for anomaly detection andlocalization, extensive experiments are conducted on three widely-used anomalydetection datasets. The encouraging performance of the proposed method comparedto the recent approaches based on the memory module demonstrates itssuperiority.

Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation