Visual anomaly detection is vital in real-world applications, such as
industrial defect detection and medical diagnosis. However, most existing
methods focus on local structural anomalies and fail to detect higher-level
functional anomalies under logical conditions. Although recent studies have
explored logical anomaly detection, they can only address simple anomalies like
missing or addition and show poor generalizability due to being heavily
data-driven. To fill this gap, we propose SAM-LAD, a zero-shot, plug-and-play
framework for logical anomaly detection in any scene. First, we obtain a query
image's feature map using a pre-trained backbone. Simultaneously, we retrieve
the reference images and their corresponding feature maps via the nearest
neighbor search of the query image. Then, we introduce the Segment Anything
Model (SAM) to obtain object masks of the query and reference images. Each
object mask is multiplied with the entire image's feature map to obtain object
feature maps. Next, an Object Matching Model (OMM) is proposed to match objects
in the query and reference images. To facilitate object matching, we further
propose a Dynamic Channel Graph Attention (DCGA) module, treating each object
as a keypoint and converting its feature maps into feature vectors. Finally,
based on the object matching relations, an Anomaly Measurement Model (AMM) is
proposed to detect objects with logical anomalies. Structural anomalies in the
objects can also be detected. We validate our proposed SAM-LAD using various
benchmarks, including industrial datasets (MVTec Loco AD, MVTec AD), and the
logical dataset (DigitAnatomy). Extensive experimental results demonstrate that
SAM-LAD outperforms existing SoTA methods, particularly in detecting logical
anomalies.

为了检测任何场景中的逻辑异常，本文提出了 SAM-LAD 框架，它是一个零 - shot、即插即用的框架。通过使用预训练的主干网来获取查询图像的特征图，并通过最近邻搜索检索引用图像及其对应的特征图，然后引入 Segment Anything 模型来获得查询图像和引用图像的对象掩模。使用对象匹配模型和动态通道图注意模块进行对象匹配，最后提出了一种用于检测带有逻辑异常的对象的异常测量模型。实验结果表明，SAM-LAD 在检测逻辑异常方面表现优于现有的先进方法。

SAM-LAD：Segment Anything Model meets Zero-Shot Logic Anomaly Detection

SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection

Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a
node is anomalous or not. One common observation we made from previous
unsupervised methods is that they not only assume the absence of such anomaly
labels, but also the absence of class labels (the class a node belongs to used
in a general node classification task). In this work, we study the utility of
class labels for unsupervised GAD; in particular, how they enhance the
detection of structural anomalies. To this end, we propose a Class Label-aware
Graph Anomaly Detection framework (CLAD) that utilizes a limited amount of
labeled nodes to enhance the performance of unsupervised GAD. Extensive
experiments on ten datasets demonstrate the superior performance of CLAD in
comparison to existing unsupervised GAD methods, even in the absence of
ground-truth class label information. The source code for CLAD is available at
https://github.com/jhkim611/CLAD.

研究了无监督的图异常检测方法中类标签的作用，介绍了一个利用有限量带有标签的节点以提升无监督图异常检测性能的 Class Label-aware Graph Anomaly Detection 框架 (CLAD)。实验证明了 CLAD 在无真实类标签信息的情况下相比现有无监督图异常检测方法具有卓越的性能。