Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

You might also read

Related Articles

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

Sort by

Same author

Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach.

Journal of imaging·2024

Same author

Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework.

Journal of imaging·2023

Same author

A Lightweight Image Encryption Algorithm Based on Chaotic Map and Random Substitution.

Entropy (Basel, Switzerland)·2023

Same author

Automatic 360° Mono-Stereo Panorama Generation Using a Cost-Effective Multi-Camera System.

Sensors (Basel, Switzerland)·2020

Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026

Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026

Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026

Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026

Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026

Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 1, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

A 3DCNN-Based Knowledge Distillation Framework for Human Activity Recognition.

Hayat Ullah¹, Arslan Munir¹

¹Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA.

Journal of Imaging

|April 27, 2023

Summary

This summary is machine-generated.

This study introduces a knowledge distillation framework for human action recognition, significantly improving accuracy and inference speed. The method efficiently transfers knowledge from a large teacher model to a smaller student model for real-time applications.

Keywords:

3DCNN deep learning deep neural networks human action recognition knowledge distillation

More Related Videos

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

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

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Aug 1, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

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

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

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Human action recognition is crucial for video analytics, requiring analysis of complex sequential patterns in human movements.
Existing methods often struggle with balancing accuracy and computational efficiency for real-time applications.

Purpose of the Study:

To propose an effective knowledge distillation framework for enhancing human action recognition.
To distill spatio-temporal knowledge from a large teacher model to a lightweight student model.

Main Methods:

Developed an offline knowledge distillation framework using a pre-trained 3D Convolutional Neural Network (3DCNN) teacher and a lightweight 3DCNN student model.
Trained only the student model to mimic the teacher's prediction accuracy.

Main Results:

Achieved up to 35% improvement in accuracy compared to state-of-the-art methods on four benchmark datasets.
Demonstrated up to 50x improvement in inference speed (Frames Per Second - FPS) over existing methods.

Conclusions:

The proposed knowledge distillation framework offers a robust and efficient solution for human action recognition.
The method's high accuracy and fast inference times make it suitable for real-time human activity recognition applications.