Sep 25, 2022 · We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core ...
We exhibit the generality and flexibility of SSMCTB by integrating it into multiple state-of-the-art neural models for anomaly detection, bringing forth ...
In this article, we propose to integrate the reconstruction-based functionality into a novel self-supervised masked convolutional transformer block.
A novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level ...
Sep 4, 2024 · Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while ...
We exhibit the generality and flexibility of SSMCTB by integrating it into multiple state-of-the-art neural models for anomaly detection, bringing forth ...
View recent discussion. Abstract: Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of ...
7 days ago · Furthermore, we show that our block is applicable to a wider variety of tasks, adding anomaly detection in medical images and thermal videos to ...
Jan 1, 2024 · We exhibit the generality and flexibility of SSMCTB by integrating it into multiple state-of-the-art neural models for anomaly detection, ...
More specifically, we design a novel block based on masked convolution and channel at- tention to reconstruct a masked part of the convolutional receptive field ...