TC-MAF: Train-Calibrated Bounded Multi-Evidence Fusion for Multimodal Industrial Anomaly Detection

2026-07-13

AI Summary

This work addresses the unreliability of RGB reconstruction evidence across diverse product categories and the absence of category-adaptive fusion strategies during testing in multimodal industrial anomaly detection. To overcome these limitations, the authors propose TC-MAF, a unified anchor-based multimodal evidence fusion framework that integrates multimodal detectors, complementary evidence from Dinomaly, and cross-modal consistency cues. Notably, TC-MAF introduces a lightweight Training-time Discrete Confidence (TDC) mechanism—relying solely on normal training statistics—to dynamically adjust the weights of auxiliary evidence without requiring category information at test time, thereby enabling robust and reliable fusion. The method achieves state-of-the-art average performance among multimodal approaches, with an image-level AUROC of 0.979 and pixel-level AUPRO of 0.990 on MVTec-3D, and demonstrates consistent robustness across various ablations and cross-dataset evaluations.

Abstract

Multimodal anomaly detection benefits from complementary RGB and 3D evidence, yet auxiliary RGB reconstruction is not equally reliable across product categories and class-wise test-time policy selection is usually unavailable. We propose TC-MAF, a base-anchored multi-evidence fusion design that combines a multimodal detector, complementary Dinomaly evidence, and a small cross-modal consistency cue under one fixed pixel-level fusion formula. A lightweight training-dispersion confidence (TDC) term scales auxiliary participation using only normal training statistics. On MVTec-3D, TC-MAF reaches 0.979 image-level AUROC and 0.990 pixel-level AUPRO, achieving the best mean results on both detection and localization among the compared multimodal methods. Systematic ablations show that the fusion structure itself is the dominant factor, while TDC provides a smaller but reproducible calibration gain over no calibration or arbitrary calibration. Additional experiments show that the same design remains effective under a pooled-statistics variant, auxiliary-branch and backbone substitutions, few-shot settings, a missing-3D setting, and cross-dataset evaluation on Eyecandies. Code is available at https://anonymous.4open.science/r/TC_MAF-C3BB.

Problem

• multimodal anomaly detection
• evidence fusion
• RGB-3D reconstruction
• industrial inspection
• test-time calibration

Innovation

• multi-evidence fusion
• train-calibrated confidence
• multimodal anomaly detection
• cross-modal consistency
• TDC