Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection

@article{Madan2022SelfSupervisedMC,
  title={Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection},
  author={Neelu Madan and Nicolae-Catalin Ristea and Radu Tudor Ionescu and Kamal Nasrollahi and Fahad Shahbaz Khan and Thomas Baltzer Moeslund and Mubarak Shah},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.12148}
}
—Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of applications ranging from product fault detection on industrial production lines and impending event detection in video surveillance to finding lesions in medical scans. Regardless of the domain, anomaly detection is typically framed as a one-class classification task, where the learning is conducted on normal examples only. An entire family of successful anomaly detection… 

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