Fine-Grained Enhancement Convolutional Diffusion Transformer for Unsupervised Anomaly Detection

Reconstruction-based methods have achieved excellent performance in anomaly detection. Diffusion models are considered highly suitable for anomaly detection tasks due to their strong ability in reconstruction. Nevertheless, diffusion-based models require the reconstruction of noise features, which may lack the capacity for fine-grained feature reconstruction and fail to provide adequate semantic information for reconstruction guidance. To solve the aforementioned problems, this paper proposes a Fine-Grained Enhancement Convolutional Diffusion Transformer Anomaly Detection (FECDTAD) framework for multi-class anomaly detection. The core model of the proposed framework is the Fine-Grained Enhancement Convolutional Denoising Transformer (FECDT), which employs the diffusion transformer paradigm. To enhance fine-grained reconstruction in the diffusion process, the FECDTAD adopts a series of feature information fusion strategies. Specifically, to enhance both fine-grained perception and global understanding, the FECDT model employs a simple feature fusion module to integrate shallow-level and deep-level features extracted from a pre-trained vision transformer. To enhance the capacity for fine-grained feature reconstruction, the FECDT integrates local and global information via a CNN-Transformer architecture. Moreover, to provide guidance for the reconstruction of anomalous areas, semantic information is propagated into the FECDT through a Cross-Attention module. Experimental results demonstrate that the proposed method is effective and can surpass the state-of-the-art methods.