Learning Constrained Embeddings for Anomaly Region Localization

Abstract

Industrial Anomaly Detection (IAD) aims to determine whether a product image contains defects and to localize anomalous regions. Since real anomalies are scarce, diverse, and difficult to acquire, many IAD methods rely on synthetic anomalies for training. However, localization performance heavily depends on how well synthetic anomalies align with real-world defects. To address this challenge, this study proposes MARC (Marking Anomaly Regions with Constraints), a vision-only, self-supervised framework for pixel-level industrial anomaly localization. MARC generates synthetic anomalies and corresponding masks for training and introduces Class-Aware Look-Twice Feature Matching (CA-LTFM), which leverages global class semantics from a pretrained image encoder and combines multi-level patch features with learnable anchors to construct anomaly and normal prototypes for patch-to-prototype matching. Classification loss, segmentation loss, and disentanglement loss are jointly optimized to enable anomaly classification, localization, and anomaly/normal feature separation. Experiments on the MVTec-AD and VisA datasets demonstrate that MARC achieves improved synthetic-to-real transferability and more consistent performance between anomaly classification and localization than existing VLM-based approaches. While class-aware prototype matching and prototype disentanglement provide more transferable anomaly representations under fully synthetic training settings, the results further show that the synthetic-to-real domain gap remains a key challenge.