XMatchAD: A Cross-Modal Matching Perspective on Reconstruction-based Anomaly Detection

2026-07-26

AI Summary

This work addresses the limitations of existing reconstruction-based unsupervised anomaly detection methods, which struggle to capture subtle anomalies and precisely delineate ambiguous boundaries, particularly in multi-class settings. To overcome these challenges, the study introduces a novel pseudo cross-modal matching framework that treats the input and reconstructed images as complementary modalities. By leveraging an attention-guided cross-modal matching mechanism and an adaptive frequency-aware fusion module, the method explicitly models anomaly-relevant patterns and enhances fine-grained detail perception through multi-scale high-frequency component coupling. Extensive experiments demonstrate state-of-the-art performance on the MVTec-AD, VisA, and MPDD benchmarks, significantly outperforming current best approaches and exhibiting superior capability in both complex anomaly detection and precise localization.

Abstract

The remarkable success of reconstruction-based methods in Unsupervised Anomaly Detection (UAD) lies in their ability to identify and localize anomalies by modeling discrepancies between input images and their reconstructed counterparts. However, these approaches often struggle to capture subtle anomalies and tend to produce blurred anomaly boundaries, which significantly limits their effectiveness, particularly in complex multi-class scenarios. To address these issues, we present XMatchAD, a novel UAD framework that reinterprets the task from a pseudo cross-modal matching perspective. Specifically, the input and reconstructed images are treated as two complementary modalities and their matching relationships are precisely exploited for anomaly detection. First, a pre-trained feature extractor is employed to encode discriminative representations. Second, an attention-guided cross-modal matching mechanism is introduced to match local inter-modal anomaly-related patterns while mutually refining the features. This enhances the sensitivity to anomalies with diverse shapes and subtle deviations and significantly improves the precision of anomaly detection and localization. Third, we design an adaptive frequency-aware fusion module that further delineates sharp anomaly boundaries through the coupling of high-frequency components from cross-modal multi-scale representations. Comprehensive evaluations on MVTec-AD, VisA, and MPDD benchmarks demonstrate that our method consistently achieves superior performance, outperforming state-of-the-art methods in multi-class anomaly detection and localization. The code will be released at https://github.com/Mingxiu-Cai/XMatchAD.

Problem

• Unsupervised Anomaly Detection
• reconstruction-based methods
• subtle anomalies
• blurred anomaly boundaries
• multi-class scenarios

Innovation

• cross-modal matching
• reconstruction-based anomaly detection
• attention-guided matching
• frequency-aware fusion
• unsupervised anomaly detection