Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

Deep Learning for Video Anomaly Detection: A Review.

Peng Wu, Chengyu Pan, Yuting Yan

    IEEE Transactions on Neural Networks and Learning Systems
    |January 6, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    PLM-effector: unleashing the potential of protein language models for bacterial secreted protein prediction.

    Briefings in bioinformatics·2026
    Same author

    The Multifaceted Roles of GPR120 in Central Nervous System Disorders: Mechanistic Insights and Therapeutic Implications.

    Molecular neurobiology·2026
    Same author

    Restarting synaptic remodeling and structural network: New treatment strategies for epilepsy.

    Neural regeneration research·2026
    Same author

    Gut microbiota-neuroinflammation axis: A new mechanism and therapeutic target for comorbid depression in epilepsy.

    Brain, behavior, & immunity - health·2026
    Same author

    Study on the antibacterial activity and biocompatibility of nano-silver/carbon nanotube composite coatings on airway stents prepared by micro-transfer printing.

    Scientific reports·2025
    Same author

    Research progress on immunometabolism and gut microbiota in cryptococcal meningitis: mechanisms and therapeutic implications.

    Frontiers in neuroscience·2025
    Same journal

    Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

    IEEE transactions on neural networks and learning systems·2026
    Same journal

    A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

    IEEE transactions on neural networks and learning systems·2026
    Same journal

    Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

    IEEE transactions on neural networks and learning systems·2026
    Same journal

    cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

    IEEE transactions on neural networks and learning systems·2026
    Same journal

    Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

    IEEE transactions on neural networks and learning systems·2026
    Same journal

    Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

    IEEE transactions on neural networks and learning systems·2026
    See all related articles

    This review surveys deep learning methods for video anomaly detection (VAD), covering diverse supervision types and the latest advancements. It offers a comprehensive overview for researchers in computer vision.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Video anomaly detection (VAD) is crucial for identifying unusual events in videos.
    • Deep learning has significantly advanced VAD capabilities and applications.
    • Existing surveys lack comprehensive coverage of recent VAD methods and supervision types.

    Purpose of the Study:

    • To provide an extensive and comprehensive review of deep learning-based VAD methods.
    • To cover a wide spectrum of VAD categories, including semi-supervised, weakly supervised, fully supervised, unsupervised, and open-set supervised learning.
    • To include the latest VAD works utilizing pretrained large models and open-world learning.

    Main Methods:

    • Categorization of VAD methods based on supervision levels (semi-supervised, weakly supervised, fully supervised, unsupervised, open-set supervised).

    Related Experiment Videos

    Last Updated: Jan 13, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.0K
  • Inclusion of recent approaches using pretrained large models and open-world learning.
  • Systematic review of literature, datasets, open-source codes, and evaluation metrics.

    • Main Results:

    • A well-organized taxonomy of VAD methods is presented.
    • Characteristics and performance comparisons of different VAD approaches are discussed.
    • The review addresses limitations of previous surveys by including broader VAD categories and advanced methods.

    Conclusions:

    • A comprehensive understanding of the current VAD landscape is provided.
    • Key research directions for the VAD community are identified.
    • This review serves as a valuable resource for researchers and practitioners in video anomaly detection.