Learning Multi-view Anomaly Detection
<|reference_start|>Learning Multi-view Anomaly Detection: 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.<|reference_end|>
@article{he2024learning,
    title={Learning Multi-view Anomaly Detection},
    author={Haoyang He, Jiangning Zhang, Guanzhong Tian, Chengjie Wang, Lei Xie},
    journal={arXiv preprint arXiv:2407.11935},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11935},
    primaryClass={cs.CV}
}