Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
<|reference_start|>Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference: 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.<|reference_end|>
@article{jiang2024toward,
    title={Toward Multi-class Anomaly Detection: Exploring Class-aware Unified
  Model against Inter-class Interference},
    author={Xi Jiang, Ying Chen, Qiang Nie, Jianlin Liu, Yong Liu, Chengjie Wang
  and Feng Zheng},
    journal={arXiv preprint arXiv:2403.14213},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14213},
    primaryClass={cs.CV}
}