  This study explores the recently proposed challenging multi-view Anomaly
Detection (AD) task. Single-view tasks would encounter blind spots from other
perspectives, resulting in inaccuracies in sample-level prediction. Therefore,
we introduce the \textbf{M}ulti-\textbf{V}iew \textbf{A}nomaly
\textbf{D}etection (\textbf{MVAD}) framework, which learns and integrates
features from multi-views. Specifically, we proposed a
\textbf{M}ulti-\textbf{V}iew \textbf{A}daptive \textbf{S}election
(\textbf{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 a conducted attention mechanism for each single-view
window and the top-K most correlated multi-view windows. Adjusting the window
sizes and top-K can minimise the computational complexity to linear. Extensive
experiments on the Real-IAD dataset for cross-setting (multi/single-class)
validate the effectiveness of our approach, achieving state-of-the-art
performance among sample \textbf{4.1\%}$\uparrow$/ image
\textbf{5.6\%}$\uparrow$/pixel \textbf{6.7\%}$\uparrow$ levels with a total of
ten metrics with only \textbf{18M} parameters and fewer GPU memory and training
time.
