Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models
Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance
systems, enabling the temporal or spatial identification of anomalous events within videos …
systems, enabling the temporal or spatial identification of anomalous events within videos …
Hierarchical semantic contrast for scene-aware video anomaly detection
Increasing scene-awareness is a key challenge in video anomaly detection (VAD). In this
work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware …
work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware …
Graph neural networks in IoT: A survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
Localized sparse incomplete multi-view clustering
Incomplete multi-view clustering, which aims to solve the clustering problem on the
incomplete multi-view data with partial view missing, has received more and more attention …
incomplete multi-view data with partial view missing, has received more and more attention …
Amp-net: Appearance-motion prototype network assisted automatic video anomaly detection system
As essential tools for industry safety protection, automatic video anomaly detection systems
(AVADS) are designed to detect anomalous events of concern in surveillance videos …
(AVADS) are designed to detect anomalous events of concern in surveillance videos …
SSMTL++: Revisiting self-supervised multi-task learning for video anomaly detection
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was
recently introduced in literature. Due to its highly accurate results, the method attracted the …
recently introduced in literature. Due to its highly accurate results, the method attracted the …
Self-supervision-augmented deep autoencoder for unsupervised visual anomaly detection
Deep autoencoder (AE) has demonstrated promising performances in visual anomaly
detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield …
detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield …
Deep learning for time-series prediction in IIoT: progress, challenges, and prospects
Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable
intelligent process control, analysis, and management, such as complex equipment …
intelligent process control, analysis, and management, such as complex equipment …
A review of deep learning for video captioning
Video captioning (VC) is a fast-moving, cross-disciplinary area of research that comprises
contributions from domains such as computer vision, natural language processing …
contributions from domains such as computer vision, natural language processing …