MAEDAY: MAE for few- and zero-shot AnomalY-Detection

We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous regions are harder to reconstruct compared with normal regions. MAEDAY is the first image-reconstruction-based anomaly detection method that u...

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Vydáno v:	Computer vision and image understanding Ročník 241; s. 103958
Hlavní autoři:	Schwartz, Eli, Arbelle, Assaf, Karlinsky, Leonid, Harary, Sivan, Scheidegger, Florian, Doveh, Sivan, Giryes, Raja
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 01.04.2024
Témata:	Anomaly-detection Foreign object detection Masked autoencoder Masked autoencoder 41A10 65D05 65D17 Anomaly-detection Foreign object detection 41A05
ISSN:	1077-3142, 1090-235X
On-line přístup:	Získat plný text
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Popis
Shrnutí:	We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous regions are harder to reconstruct compared with normal regions. MAEDAY is the first image-reconstruction-based anomaly detection method that utilizes a pre-trained model, enabling its use for Few-Shot Anomaly Detection (FSAD). We also show the same method works surprisingly well for the novel tasks of Zero-Shot AD (ZSAD) and Zero-Shot Foreign Object Detection (ZSFOD), where no normal samples are available. •Pre-training MAE on an arbitrary set of images and using it for Anomaly-Detection.•Suggesting the new task of Zero-Shot Anomaly-Detection and demonstrating strong results.•Demonstrating strong results for the new task of Zero-Shot Foreign Object Detection.•Releasing a new Foreign Object Detection dataset.
ISSN:	1077-3142 1090-235X
DOI:	10.1016/j.cviu.2024.103958