S3AD: Efficient industrial anomaly detection via selection, space, and scale

Real-world industrial visual anomaly detection (VAD) demands the simultaneous handling of diverse product categories under strict real-time constraints. However, the self-attention mechanism inherent in standard Vision Transformers (ViTs) incurs quadratic computational complexity during global context modeling, thereby limiting their real-time applicability in VAD. Crucially, this bottleneck is largely driven by the uniform processing of massive background redundancy common in industrial scenarios. To bypass this inefficiency while retaining high precision, we propose S 3 AD , a unified framework anchored in Selection, Space, and Scale. Instead of dense global interactions, S 3 AD employs a Salience-Guided Sparse Attention to focus resources solely on salient areas. Specifically, regarding Selection, we devise a discriminating Top-K pruning strategy guided by a hybrid metric of attention scores and feature magnitudes, which filters out non-informative tokens to minimize cost. Simultaneously, for Space, we incorporate Geometry-Preserving Positional Embeddings to explicitly anchor the selected tokens, ensuring structural integrity is maintained despite sparse interaction. Finally, concerning Scale, we integrate an Exchangeable Feature Pyramid Network (E-FPN) to ensure rigorous feature fusion across resolutions. Extensive experiments demonstrate that S 3 AD achieves state-of-the-art performance and establishes a superior accuracy–efficiency Pareto frontier, consistently surpassing strong baselines on multiple benchmarks. The code is available at: https://github.com/CreatedTRYNA/S3AD .