Computation–Performance Benchmark for Generalized Industrial Anomaly Detection

Recent industrial anomaly detection (IAD) research increasingly targets three generalization settings: (1) multi-class unified detection with a single shared model, (2) few-shot detection with limited normal data, and (3) zero-shot detection without a designated target-domain support set before deployment. These settings require models to generalize robustly under data-scarce, cross-scenario conditions. Although recent methods achieve high accuracy on benchmark datasets, substantial differences remain in deployment cost. Here, we present a computational taxonomy and a benchmark of 30 representative methods on MVTec-AD and VisA dataset. Evaluation on a single NVIDIA A100 combines detection and localization metrics with batch-one throughput, peak GPU memory, parameter counts, and profiled query FLOPs. Through analyses of predictive performance, few-shot stability, and computational cost, we characterize the strengths and limitations of existing methods under different generalization settings. Our findings reveal that accuracy rankings alone provide an incomplete account of practical effectiveness, motivating an evaluation perspective that connects detection quality with the state and computation required at deployment.