  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 methods is based on learning
to reconstruct masked normal inputs (e.g. patches, future frames, etc.) and
exerting the magnitude of the reconstruction error as an indicator for the
abnormality level. Unlike other reconstruction-based methods, we present a
novel self-supervised masked convolutional transformer block (SSMCTB) that
comprises the reconstruction-based functionality at a core architectural level.
The proposed self-supervised block is extremely flexible, enabling information
masking at any layer of a neural network and being compatible with a wide range
of neural architectures. In this work, we extend our previous self-supervised
predictive convolutional attentive block (SSPCAB) with a 3D masked
convolutional layer, a transformer for channel-wise attention, as well as a
novel self-supervised objective based on Huber loss. Furthermore, we show that
our block is applicable to a wider variety of tasks, adding anomaly detection
in medical images and thermal videos to the previously considered tasks based
on RGB images and surveillance videos. We exhibit the generality and
flexibility of SSMCTB by integrating it into multiple state-of-the-art neural
models for anomaly detection, bringing forth empirical results that confirm
considerable performance improvements on five benchmarks. We release our code
and data as open source at: https://github.com/ristea/ssmctb.
