Papers › TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection

TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection

16 Nov 2023arXiv:2311.09999archive 2025-07-28

Matic Fučka, Vitjan Zavrtanik, Danijel Skočaj

Surface anomaly detection is a vital component in manufacturing inspection. Current discriminative methods follow a two-stage architecture composed of a reconstructive network followed by a discriminative network that relies on the reconstruction output. Currently used reconstructive networks often produce poor reconstructions that either still contain anomalies or lack details in anomaly-free regions. Discriminative methods are robust to some reconstructive network failures, suggesting that the discriminative network learns a strong normal appearance signal that the reconstructive networks miss. We reformulate the two-stage architecture into a single-stage iterative process that allows the exchange of information between the reconstruction and localization. We propose a novel transparency-based diffusion process where the transparency of anomalous regions is progressively increased, restoring their normal appearance accurately while maintaining the appearance of anomaly-free regions using localization cues of previous steps. We implement the proposed process as TRANSparency DifFUSION (TransFusion), a novel discriminative anomaly detection method that achieves state-of-the-art performance on both the VisA and the MVTec AD datasets, with an image-level AUROC of 98.5% and 99.2%, respectively. Code: https://github.com/MaticFuc/ECCV_TransFusion

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Tasks

Anomaly DetectionDepth Anomaly Detection and SegmentationRGB+3D Anomaly Detection and Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Anomaly Detection MVTec AD TransFusion Detection AUROC 99.4 #35 of 148 Archive leaderboard report
Anomaly Detection MVTec AD TransFusion Segmentation AUPRO 95.3 #35 of 148 Archive leaderboard report
Anomaly Detection VisA TransFusion Detection AUROC 98.7 #9 of 50 Archive leaderboard report
Anomaly Detection VisA TransFusion Segmentation AUPRO (until 30% FPR) 94.7 #9 of 50 Archive leaderboard report
Depth Anomaly Detection and Segmentation MVTEC 3D-AD TransFusion Detection AUROC 0.957 #1 of 13 Archive leaderboard report
Depth Anomaly Detection and Segmentation MVTEC 3D-AD TransFusion Segmentation AUPRO 0.947 #1 of 13 Archive leaderboard report
RGB+3D Anomaly Detection and Segmentation MVTEC 3D-AD TransFusion Detection AUCROC 0.982 #1 of 9 Archive leaderboard report
RGB+3D Anomaly Detection and Segmentation MVTEC 3D-AD TransFusion Segmentation AUPRO 0.983 #1 of 9 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Diffusion

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