DAST-Net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection

Rangachary Kommanduri; Mrinmoy Ghorai

doi:10.1016/j.neucom.2024.127444

ScienceGate Book Chapters

JOURNAL ARTICLE

DAST-Net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection

Rangachary Kommanduri Mrinmoy Ghorai

Year: 2024 Journal: Neurocomputing Vol: 579 Pages: 127444-127444 Publisher: Elsevier BV

DOI: 10.1016/j.neucom.2024.127444

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Autoencoder Artificial intelligence Benchmark (surveying) Anomaly detection Residual Feature learning Pattern recognition (psychology) Representation (politics) Feature (linguistics) Convolutional neural network Deep learning Machine learning

Metrics

Cited By

15.33

FWCI (Field Weighted Citation Impact)

Refs

0.98

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Data-Driven Disease Surveillance

Health Sciences → Medicine → Epidemiology

DAST-Net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection

Metrics

Citation History

Topics

Related Documents

Dast-Net: Depth-Aware Spatio-Temporal Network for Video Deblurring

STVAD: A Lightweight Spatio–Temporal Attention Network for Video Anomaly Detection

STCA-net: spatio-temporal collaborative attention network for deepfake video detection

Normal Spatio-Temporal Information Enhance for Unsupervised Video Anomaly Detection

SASTNet: Self-Attention-Enabled Spatio-Temporal Network for single scene video anomaly detection