Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913276369043456 |
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| author | Jiang, Xi Chen, Ying Nie, Qiang Liu, Jianlin Liu, Yong Wang, Chengjie Zheng, Feng |
| author_facet | Jiang, Xi Chen, Ying Nie, Qiang Liu, Jianlin Liu, Yong Wang, Chengjie Zheng, Feng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14213 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference Jiang, Xi Chen, Ying Nie, Qiang Liu, Jianlin Liu, Yong Wang, Chengjie Zheng, Feng Computer Vision and Pattern Recognition 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. |
| title | Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.14213 |