A. S.

Explainable Video Anomaly Detection Using Video Masked Autoencoders and Convolutional LSTM

This paper presents a unified and interpretable framework for video anomaly detection that integrates a pretrained Video Masked Autoencoder (VideoMAE), a ConvLSTM-based temporal modeling module, and a 3D CNN decoder for spatiotemporal reconstruction. The model is trained exclusively on normal video sequences and identi...