  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.
