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 Concept Videos

Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...

You might also read

Related Articles

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

Sort by
Same author

A Pathology-Instructed Theranostic Platform with Mechanoadaptive and ROS-Powered Nanobreathing Functions for Precision Myocardial Repair.

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

Calceolarioside B alleviates airway barrier dysfunction and inflammation via targeting P2Y<sub>6</sub>R in OVA-triggered asthma.

Biochemical pharmacology·2026
Same author

A Cascaded Classification-Regression Framework for Shear Strength Prediction of Cold-Formed Steel Screw Connections.

Materials (Basel, Switzerland)·2026
Same author

Influencing Factor Analysis and Predictive Model Development for Platelet Transfusion Refractoriness in Pediatric Oncology Patients.

Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion·2026
Same author

Research on PM<sub>2.5</sub> prediction and spatiotemporal variation in the Yangtze River Delta region of China based on constraint-based geographically weighted random forest.

Environmental monitoring and assessment·2026
Same author

Machine learning for oral frailty factors in hospitalized schizophrenia patients: two-stage feature selection and SHAP analysis.

Frontiers in psychiatry·2026
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 11, 2026

Mixed Reality for Education MRE Implementation and Results in Online Classes for Engineering
04:12

Mixed Reality for Education MRE Implementation and Results in Online Classes for Engineering

Published on: June 23, 2023

572

IMRMB-Net: A lightweight student behavior recognition model for complex classroom scenarios.

Caihong Feng1, Zheng Luo1, Deyao Kong1

  • 1Department of Computer Science and Information Engineering, Harbin Normal University, Harbin, China.

Plos One
|March 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces IMRMB-Net, a lightweight model for student behavior recognition. It enhances accuracy for occluded and small objects in classrooms, improving teaching quality.

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.5K
Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

844

Related Experiment Videos

Last Updated: May 11, 2026

Mixed Reality for Education MRE Implementation and Results in Online Classes for Engineering
04:12

Mixed Reality for Education MRE Implementation and Results in Online Classes for Engineering

Published on: June 23, 2023

572
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.5K
Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

844

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Educational Technology

Background:

  • Classroom behavior analysis is crucial for improving education quality.
  • Existing methods struggle with occlusion, small objects, and environmental interference, leading to low accuracy.
  • There is a need for accurate and computationally efficient student behavior recognition models.

Purpose of the Study:

  • To propose a lightweight student behavior recognition model, IMRMB-Net, to address challenges in accuracy and performance.
  • To improve the recognition of occluded and small objects in classroom settings.
  • To enhance the overall robustness and efficiency of student behavior analysis.

Main Methods:

  • Developed a lightweight feature extraction module, Inverted Residual Mobile Block (IMRMB).
  • Implemented DySample in the neck network to improve small object recognition.
  • Designed a novel Focaler-ShapeIoU loss function to enhance model robustness and occlusion handling.

Main Results:

  • IMRMB-Net achieved high accuracy (mAP@50=93.3%, mAP@50:95=78.7%) and lightweight performance (FPS=60.37, Params=7.32MB).
  • Effectively addressed occlusion problems in classroom scenarios on UK_Dataset and SCB_Dataset.
  • Demonstrated strong generalization and small target recognition capabilities on the VisDrone2021 dataset.

Conclusions:

  • IMRMB-Net offers a promising solution for accurate and efficient student behavior recognition in educational settings.
  • The model's design effectively tackles key challenges like occlusion and small object detection.
  • This research contributes to advancing educational technology through improved classroom behavior analysis.