Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …

The current advancement towards retinal disease detection mainly focused on distinct
feature extraction using either a convolutional neural network (CNN) or a transformer-based …

Multimedia anomaly datasets play a crucial role in automated surveillance. They have a
wide range of applications expanding from outlier objects/situation detection to the detection …

In the face of detection problems posed by complex textile texture backgrounds, different
sizes, and different types of defects, commonly used object detection networks have …

Video anomaly detection algorithms are yet to advance at the pace CCTV footage data of
public places is being recorded and made publicly available. An anomaly specifies unusual …

Video classification has achieved remarkable success in recent years, driven by advanced
deep learning models that automatically categorize video content. This paper provides a …

The main challenge faced by video‐based real‐world anomaly detection systems is the
accurate learning of unusual events that are irregular, complicated, diverse, and …

Abstract We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection a
novel video anomaly detection methodology that combines pseudo-anomalies dynamic …

The growing demand for high-quality industrial products has led to a significant emphasis on
image anomaly detection (AD). AD in industrial goods presents a formidable research …

Accurate short-term forecasting of electrical energy loads is essential for optimizing energy
management in low-carbon buildings. This research presents an innovative two-stage …