  Logical anomalies (LA) refer to data violating underlying logical constraints
e.g., the quantity, arrangement, or composition of components within an image.
Detecting accurately such anomalies requires models to reason about various
component types through segmentation. However, curation of pixel-level
annotations for semantic segmentation is both time-consuming and expensive.
Although there are some prior few-shot or unsupervised co-part segmentation
algorithms, they often fail on images with industrial object. These images have
components with similar textures and shapes, and a precise differentiation
proves challenging. In this study, we introduce a novel component segmentation
model for LA detection that leverages a few labeled samples and unlabeled
images sharing logical constraints. To ensure consistent segmentation across
unlabeled images, we employ a histogram matching loss in conjunction with an
entropy loss. As segmentation predictions play a crucial role, we propose to
enhance both local and global sample validity detection by capturing key
aspects from visual semantics via three memory banks: class histograms,
component composition embeddings and patch-level representations. For effective
LA detection, we propose an adaptive scaling strategy to standardize anomaly
scores from different memory banks in inference. Extensive experiments on the
public benchmark MVTec LOCO AD reveal our method achieves 98.1% AUROC in LA
detection vs. 89.6% from competing methods.
