EPAComp: An Architectural Model for EPA Composition
Luís Henrique Neves Villaça, Sean Wolfgand Matsui Siqueira, et al.
SBSI 2023
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.
Luís Henrique Neves Villaça, Sean Wolfgand Matsui Siqueira, et al.
SBSI 2023
Arnon Amir, M. Lindenbaum
Computer Vision and Image Understanding
Sharat Chikkerur, Venu Govindaraju, et al.
WACV 2005
Yaniv Altshuler, Vladimir Yanovski, et al.
ICARA 2009