Within (semi-)automated visual inspection, learning-based approaches for
assessing visual defects, including deep neural networks, enable the processing
of otherwise small defect patterns in pixel size on high-resolution imagery.
The emergence of these often rarely occurring defect patterns explains the
general need for labeled data corpora. To not only alleviate this issue but to
furthermore advance the current state of the art in unsupervised visual
inspection, this contribution proposes a DifferNet-based solution enhanced with
attention modules utilizing SENet and CBAM as backbone - AttentDifferNet - to
improve the detection and classification capabilities on three different visual
inspection and anomaly detection datasets: MVTec AD, InsPLAD-fault, and
Semiconductor Wafer. In comparison to the current state of the art, it is shown
that AttentDifferNet achieves improved results, which are, in turn, highlighted
throughout our quantitative as well as qualitative evaluation, indicated by a
general improvement in AUC of 94.34 vs. 92.46, 96.67 vs. 94.69, and 90.20 vs.
88.74%. As our variants to AttentDifferNet show great prospects in the context
of currently investigated approaches, a baseline is formulated, emphasizing the
importance of attention for anomaly detection.

提出一种基于 DifferNet 的解决方案，利用 SENet 和 CBAM 作为骨干网络的注意力模块，通过对三个不同的视觉检测和异常检测数据集（MVTec AD、InsPLAD 故障和半导体晶片）进行改进，提高了检测和分类能力，并通过定量和定性评估显示了改进的结果。

注意力模块提升现代图像级异常检测：DifferNet 案例研究

Attention Modules Improve Modern Image-Level Anomaly Detection: A  DifferNet Case Study

The unsupervised visual inspection of defects in industrial products poses a
significant challenge due to substantial variations in product surfaces.
Current unsupervised models struggle to strike a balance between detecting
texture and object defects, lacking the capacity to discern latent
representations and intricate features. In this paper, we present a novel
self-supervised learning algorithm designed to derive an optimal encoder by
tackling the renowned jigsaw puzzle. Our approach involves dividing the target
image into nine patches, tasking the encoder with predicting the relative
position relationships between any two patches to extract rich semantics.
Subsequently, we introduce an affinity-augmentation method to accentuate
differences between normal and abnormal latent representations. Leveraging the
classic support vector data description algorithm yields final detection
results. Experimental outcomes demonstrate that our proposed method achieves
outstanding detection and segmentation performance on the widely used MVTec AD
dataset, with rates of 95.8% and 96.8%, respectively, establishing a
state-of-the-art benchmark for both texture and object defects. Comprehensive
experimentation underscores the effectiveness of our approach in diverse
industrial applications.

本文提出了一种新颖的自监督学习算法，通过解决著名的拼图问题来设计一个优化的编码器，以在工业产品中检测纹理和物体缺陷，实验结果表明我们的方法在广泛使用的 MVTec AD 数据集上达到了 95.8% 和 96.8% 的杰出检测和分割性能，为纹理和物体缺陷建立了最新的基准。

自监督上下文学习用于工业缺陷的视觉检测

Self-supervised Context Learning for Visual Inspection of Industrial  Defects

Within (semi-)automated visual industrial inspection, learning-based
approaches for assessing visual defects, including deep neural networks, enable
the processing of otherwise small defect patterns in pixel size on
high-resolution imagery. The emergence of these often rarely occurring defect
patterns explains the general need for labeled data corpora. To alleviate this
issue and advance the current state of the art in unsupervised visual
inspection, this work proposes a DifferNet-based solution enhanced with
attention modules: AttentDifferNet. It improves image-level detection and
classification capabilities on three visual anomaly detection datasets for
industrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In
comparison to the state of the art, AttentDifferNet achieves improved results,
which are, in turn, highlighted throughout our quali-quantitative study. Our
quantitative evaluation shows an average improvement - compared to DifferNet -
of 1.77 +/- 0.25 percentage points in overall AUROC considering all three
datasets, reaching SOTA results in InsPLAD-fault, an industrial inspection
in-the-wild dataset. As our variants to AttentDifferNet show great prospects in
the context of currently investigated approaches, a baseline is formulated,
emphasizing the importance of attention for industrial anomaly detection both
in the wild and in controlled environments.

通过注意力机制和深度神经网络，提出了一种改进的 DifferNet 解决方案：AttentDifferNet，它在三个工业检测数据集中实现了改进的图像级缺陷检测和分类性能，达到了业界最新水平，突出了注意力在工业异常检测中的重要性。