Cross-View Dynamic Learning-Based Multi-Class Industrial Anomaly Detection

doi:10.1109/tcsvt.2026.3685706

Cross-View Dynamic Learning-Based Multi-Class Industrial Anomaly Detection

2026-04-20

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DOI：10.1109/tcsvt.2026.3685706

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Industrial anomaly detection plays a crucial role in smart manufacturing. Traditional methods typically train separate models for each category, leading to substantial memory demands and computational cost. Moreover, relying solely on single-view images is prone to detection blind spots and poor sensitivity to subtle defects. To address these problems, this study proposes CVDL, a cross-view dynamic learning-based multi-class industrial anomaly detection method. Specifically, the CVDL leverages a proposed cross-view dynamic attention in conjunction with intra-view self-attention to dynamically modulate the model’s attention on multi-view information, thereby enhancing the detection performance of subtle defects. Furthermore, a category-guided prompt is developed to utilize object category information, which improves the model’s class-aware detection accuracy. To enhance the model’s robustness, we introduce a structured noise injection strategy and a region-wise mask into the CVDL, mitigating the “identity shortcut” that preserves anomalies during reconstruction. Extensive experiments on the authentic multi-view industrial datasets (Real-IAD) and well-known datasets (MVTec-AD and VisA) confirm the superior detection capability and robustness of the proposed CVDL, and the overall performance of CVDL is superior to all advanced approaches on Real-IAD, achieving SoTA performance of 90.1% image-level and 99.0% pixel-level AUROC. The code will be available at https://github.com/zfinn1/CVDL.git

Keywords:

Cross-view dynamic attention

multi-class industrial anomaly detection

multi-view feature learning

Cross-View Dynamic Learning-Based Multi-Class Industrial Anomaly Detection

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