Learning Multi-View Anomaly Detection With Efficient Adaptive Selection

doi:10.1109/TMM.2026.3660076

Learning Multi-View Anomaly Detection With Efficient Adaptive Selection

2026-02-03

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DOI：10.1109/TMM.2026.3660076

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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 the Multi-View Anomaly Detection (MVAD) approach, which learns and integrates features from multi-views. Specifically, we propose a Multi-View Adaptive Selection (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$\uparrow$ across 10 metrics at the sample/image/pixel levels, using only 18M parameters and requiring fewer FLOPs and training time.

Keywords:

Anomaly detection

attention mechanism

multi-view learning

Learning Multi-View Anomaly Detection With Efficient Adaptive Selection

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