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arXiv · 2403.14213

Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference

Xi Jiang·Ying Chen·Qiang Nie·Jianlin Liu·Yong Liu·Chengjie Wang·Feng Zheng
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Abstract

In the context of high usability in single-class anomaly detection models, recent academic research has become concerned about the more complex multi-class anomaly detection. Although several papers have designed unified models for this task, they often overlook the utility of class labels, a potent tool for mitigating inter-class interference. To address this issue, we introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD), which leverages the fine-grained category information in the training stage. By learning the multi-class distributions, the model generates class-aware query embeddings for the transformer decoder, mitigating inter-class interference within the reconstruction model. Utilizing such an implicit neural representation network, MINT-AD can project category and position information into a feature embedding space, further supervised by classification and prior probability loss functions. Experimental results on multiple datasets demonstrate that MINT-AD outperforms existing unified training models.

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Xi Jiang, Ying Chen, Qiang Nie, Jianlin Liu, Yong Liu, Chengjie Wang, Feng Zheng. 2024-03-21. Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference. https://arxiv.org/abs/2403.14213

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