Efficient NASNetMobile-Enhanced Vision Transformer for Weakly Supervised Video Anomaly Detection

Muhammad Luqman Arif Mohamad; Mohd Amiruddin Abd Rahman; Nurisya Mohd Shah; Arun Kumar Sangaiah

doi:10.1109/jiot.2025.3625045

ScienceGate Book Chapters

JOURNAL ARTICLE

Efficient NASNetMobile-Enhanced Vision Transformer for Weakly Supervised Video Anomaly Detection

Muhammad Luqman Arif Mohamad Mohd Amiruddin Abd Rahman Nurisya Mohd Shah Arun Kumar Sangaiah

Year: 2025 Journal: IEEE Internet of Things Journal Vol: 13 (1)Pages: 536-548 Publisher: Institute of Electrical and Electronics Engineers

DOI: 10.1109/jiot.2025.3625045

Get Full-Text PDF Get Analytical Report

Keywords:

Anomaly detection Transformer Pattern recognition (psychology) Object detection Feature extraction Image segmentation

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.83

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Digital Media Forensic Detection

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Human Pose and Action Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Efficient NASNetMobile-Enhanced Vision Transformer for Weakly Supervised Video Anomaly Detection

Metrics

Topics

Related Documents

Temporal Relation-Embedded Transformer for Weakly-Supervised Video Anomaly Detection

Vision-text Enhancement Network For Weakly Supervised Video Anomaly Detection

Weakly Supervised Video Anomaly Detection via Self-Guided Temporal Discriminative Transformer

Weakly Supervised Video Anomaly Detection via Transformer-Enabled Temporal Relation Learning

VadCLIP++: Dynamic vision-language model for weakly supervised video anomaly detection