Learning Multi-View Anomaly Detection With Efficient Adaptive Selection

This study explores the recently proposed and challenging multi-view Anomaly Detection (AD) task. Single-view tasks will encounter blind spots from other perspectives, resulting in inaccuracies in sample-level prediction. Therefore, we introduce theMulti-ViewAnomalyDetection (MVAD) approach, which learns and integrates features from multi-views. Specifically, we propose aMulti-ViewAdaptiveSelection (MVAS) algorithm for feature learning and fusion across multiple views. The feature maps are divided into neighbourhood attention windows to calculate a semantic correlation matrix between single-view windows and all other views, which is an attention mechanism conducted for each single-view window and the top-$k$most correlated multi-view windows. Adjusting the window sizes and top-$k$can minimise the complexity to$O((hw)^\frac{4}{3})$. Extensive experiments on the Real-IAD dataset under the multi-class setting validate the effectiveness of our approach, achieving state-of-the-art performance with an average improvement of+2.5$\uparrow$across10 metricsat the sample/image/pixel levels, using only18Mparameters and requiring fewer FLOPs and training time. The codes are available athttps://github.com/lewandofskee/MVAD.