Accurate Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers

Recent advancements in industrial anomaly detection (AD) have demonstrated that incorporating a small number of anomalous samples during training can significantly enhance accuracy. However, this improvement often comes at the cost of extensive annotation efforts, which are impractical for many real-world applications. In this paper, we introduce a novel framework, "Weakly-supervised RESidual $T$ ransformer" (WeakREST), designed to achieve high anomaly detection accuracy while minimizing the reliance on manual annotations. First, we reformulate the pixel-wise anomaly localization task into a block-wise classification problem. Second, we introduce a residual-based feature representation called "Positional $F$ ast $A$ nomaly $R$ esiduals" (PosFAR) which captures anomalous patterns more effectively. To leverage this feature, we adapt the Swin Transformer for enhanced anomaly detection and localization. Additionally, we propose a weak annotation approach utilizing bounding boxes and image tags to define anomalous regions. This approach establishes a semi-supervised learning context that reduces the dependency on precise pixel-level labels. To further improve the learning process, we develop a novel ResMixMatch algorithm, capable of handling the interplay between weak labels and residual-based representations. On the benchmark dataset MVTec-AD, our method achieves an Average Precision (AP) of 83.0%, surpassing the previous best result of 82.7% in the unsupervised setting. In the supervised AD setting, WeakREST attains an AP of 87.6%, outperforming the previous best of 86.0%. Notably, even when using weaker annotations such as bounding boxes, WeakREST exceeds the performance of leading methods relying on pixel-wise supervision, achieving an AP of 87.1% compared to the prior best of 86.0% on MVTec-AD. This superior performance is consistently replicated across other well-established AD datasets, including MVTec 3D, KSDD2 and Real-IAD. Code is available at: https://github.com/BeJane/Semi_REST.