  Recent advancements in industrial Anomaly Detection (AD) have shown that
incorporating a few anomalous samples during training can significantly boost
accuracy. However, this performance improvement comes at a high cost: extensive
annotation efforts, which are often impractical in real-world applications. In
this work, we propose a novel framework called "Weakly-supervised RESidual
Transformer" (WeakREST), which aims to achieve high AD accuracy while
minimizing the need for extensive annotations. First, we reformulate the
pixel-wise anomaly localization task into a block-wise classification problem.
By shifting the focus to block-wise level, we can drastically reduce the amount
of required annotations without compromising on the accuracy of anomaly
detection Secondly, we design a residual-based transformer model, termed
"Positional Fast Anomaly Residuals" (PosFAR), to classify the image blocks in
real time. We further propose to label the anomalous regions using only
bounding boxes or image tags as weaker labels, leading to a semi-supervised
learning setting. On the benchmark dataset MVTec-AD, our proposed WeakREST
framework achieves a remarkable Average Precision (AP) of 83.0%, significantly
outperforming the previous best result of 75.8% in the unsupervised setting. In
the supervised AD setting, WeakREST further improves performance, attaining an
AP of 87.6% compared to the previous best of 78.6%. Notably, even when
utilizing weaker labels based on bounding boxes, WeakREST surpasses recent
leading methods that rely on pixel-wise supervision, achieving an AP of 87.1%
against the prior best of 78.6% on MVTec-AD. This precision advantage is also
consistently observed on other well-known AD datasets, such as BTAD and KSDD2.
