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 Videos

Region-Prompt Guided Anomaly Detection with Entropy-Based Consistency Modeling.

Yong Shi, Ruijie Xu, Zhiquan Qi

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 9, 2026
    PubMed
    Summary

    This study introduces RPGAD, a novel framework for multi-class visual industrial anomaly detection. RPGAD effectively addresses cross-class confusion and pixel similarity issues, improving detection accuracy in complex scenarios.

    Related Concept Videos

    Entropy Changes Accompanying Specific Processes01:21

    Entropy Changes Accompanying Specific Processes

    Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...

    You might also read

    Related Articles

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

    Sort by
    Same author

    Unraveling Bone-Skin Crosstalk Enables miRNA Nanoformulation for Cutaneous Neurovascular Reconstruction in Diabetic Mice.

    Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
    Same author

    Quantitative metabolomics and machine learning identify differential metabolomic biomarkers across normoglycaemia, pre-diabetes and diabetes in a Qinghai Plateau population.

    BMJ public health·2026
    Same author

    Epidemiological and genetic characteristics of a sapovirus GI.6 acute gastroenteritis outbreak in Jiangxi, China.

    Scientific reports·2026
    Same author

    Author Correction: Exercise alleviates cognitive dysfunction in Alzheimer's disease mice via skeletal muscle-derived extracellular vesicles that enhance plaque clearance by microglia.

    Nature aging·2026
    Same author

    Developmental stage-specific localization of CK1α protein in the mouse ovary.

    Acta histochemica·2026
    Same author

    Demographic history and natural selection regimes shape divergent genomic architectures in two endangered island oaks.

    The Plant journal : for cell and molecular biology·2026

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Industrial Automation

    Background:

    • Visual industrial anomaly detection has advanced to complex multi-class settings.
    • Existing methods struggle with cross-class semantic confusion and pixel similarity failures.
    • These limitations hinder accurate anomaly detection in diverse industrial applications.

    Purpose of the Study:

    • To propose RPGAD (Region-Prompt Guided Anomaly Detection), an information-theoretic framework for robust multi-class anomaly detection.
    • To model anomalies as semantic predictive instability using dual-path responses.
    • To overcome limitations of current approaches in handling complex visual patterns and inter-class similarities.

    Main Methods:

    • Developed DPENet (Dual-Path regional Energy evaluation Network) for region prompt generation via entropy-guided energy.

    Related Experiment Videos

  • Integrated RDNet (Reverse Distillation Network) for selective reconstruction of prompted regions.
  • Employed Prototype-Contrastive Optimal Transport (PCOT) loss for enhanced inter-class separability and feature aggregation.

    • Main Results:

    • RPGAD demonstrated strong performance across five benchmarks (MVTecAD, VisA, BTAD, MPDD, Real-IAD).
    • Achieved high mean Anomaly Detection (mAD) scores on all tested datasets.
    • Showcased significant pixel-level Average Precision (AP) and F1-max gains over baseline methods.

    Conclusions:

    • RPGAD provides accurate and robust multi-class anomaly detection and localization.
    • The proposed framework effectively handles complex visual scenarios and semantic confusions.
    • RPGAD represents a significant advancement in industrial anomaly detection technology.