[P] Pseudo Label Generation for Unsupervised Video Anomaly Detection

I am trying to implement this paper: ([https://arxiv.org/pdf/2203.03962.pdf](https://arxiv.org/pdf/2203.03962.pdf)) for unsupervised video anomaly detection.

The gist of the paper seems to be:

\- Create a dataset for an unsupervised setting, by mixing up the train and anomalous videos (section 4)

\- Divide each video into $p(=16)$ segments each, so for video $i$ we have the segments $s\_{ij}$, $j= 1,2...p$.

\- Using a Feature Extractor (ResNext) ,compute a '$d$' dimensional feature vector $f\_{ij}$ for each $s\_{ij}$. (Section 3.1)

\- Use this to …

anomaly anomaly detection detection generation machinelearning unsupervised video