TAMCA: Time-Aware Multi-Scale Convolutional Attention for Multi-Class Industrial Anomaly Detection

Abstract

In multi-class industrial anomaly detection, latent diffusion models commonly use the same attention and receptive-field configuration across all noise stages. This stage-invariant design makes it difficult to balance global structure recovery at high noise levels with local detail preservation at low noise levels, which may lead to structural drift, over-smoothing, and unstable anomaly maps. To address this issue, we propose TAMCA, a time-aware multi-scale convolutional attention framework for diffusion-based anomaly detection. In TAMCA, a Multi-Scale Convolutional Attention module (MSCA) is embedded into the denoising network to jointly model local textures and long-range contextual dependencies through local depthwise convolution and decomposed large-kernel convolution. To further adapt contextual modeling to different denoising stages, diffusion timestep embeddings are mapped into gating coefficients, which selectively regulate the contribution of the large-kernel context branch according to the noise level. During inference, Diffusion-aware Consistency Ensembling (DiCE) estimates multi-view reliability through median consensus and performs reliability-weighted fusion to suppress view-specific false responses. Experiments on MVTec-AD and VisA show that TAMCA achieves pixel-level AUROC values of 97.2% and 97.0%, respectively, with consistent improvements in pixel-level AP, F1max, and PRO.