Image anomaly detection and localization perform not only image-level anomaly
classification but also locate pixel-level anomaly regions. Recently, it has
received much research attention due to its wide application in various fields.
This paper proposes ProtoAD, a prototype-based neural network for image anomaly
detection and localization. First, the patch features of normal images are
extracted by a deep network pre-trained on nature images. Then, the prototypes
of the normal patch features are learned by non-parametric clustering. Finally,
we construct an image anomaly localization network (ProtoAD) by appending the
feature extraction network with $L2$ feature normalization, a $1\times1$
convolutional layer, a channel max-pooling, and a subtraction operation. We use
the prototypes as the kernels of the $1\times1$ convolutional layer; therefore,
our neural network does not need a training phase and can conduct anomaly
detection and localization in an end-to-end manner. Extensive experiments on
two challenging industrial anomaly detection datasets, MVTec AD and BTAD,
demonstrate that ProtoAD achieves competitive performance compared to the
state-of-the-art methods with a higher inference speed. The source code is
available at: this https URL

ProtoAD 是一种基于原型的神经网络，用于图像异常检测和定位，通过提取正常图像的补丁特征，并通过非参数聚类学习正常补丁特征的原型，构建了图像异常定位网络 ProtoAD，无需训练阶段即可进行端到端的异常检测和定位。

基于原型的图像异常检测和定位的神经网络

A Prototype-Based Neural Network for Image Anomaly Detection and  Localization

As one of the most challenging and practical segmentation tasks, open-world
semantic segmentation requires the model to segment the anomaly regions in the
images and incrementally learn to segment out-of-distribution (OOD) objects,
especially under a few-shot condition. The current state-of-the-art (SOTA)
method, Deep Metric Learning Network (DMLNet), relies on pixel-level metric
learning, with which the identification of similar regions having different
semantics is difficult. Therefore, we propose a method called region-aware
metric learning (RAML), which first separates the regions of the images and
generates region-aware features for further metric learning. RAML improves the
integrity of the segmented anomaly regions. Moreover, we propose a novel
meta-channel aggregation (MCA) module to further separate anomaly regions,
forming high-quality sub-region candidates and thereby improving the model
performance for OOD objects. To evaluate the proposed RAML, we have conducted
extensive experiments and ablation studies on Lost And Found and Road Anomaly
datasets for anomaly segmentation and the CityScapes dataset for incremental
few-shot learning. The results show that the proposed RAML achieves SOTA
performance in both stages of open world segmentation. Our code and appendix
are available at this https URL

本研究提出了一种叫做区域感知度量学习（RAML）的方法，通过区分图像的不同区域和生成区域感知特征从而改进模型在分割异常区域和 OUP 对象上的性能，在增量少样本学习方面达到了 SOTA 表现。