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

You might also read

Related Articles

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

Sort by
Same author

Camera Motion Agnostic Method for Estimating 3D Human Poses.

Sensors (Basel, Switzerland)·2022
Same author

Attention-Based 3D Human Pose Sequence Refinement Network.

Sensors (Basel, Switzerland)·2021
Same author

Single-Shot 3D Multi-Person Shape Reconstruction from a Single RGB Image.

Entropy (Basel, Switzerland)·2020
Same author

MeshLifter: Weakly Supervised Approach for 3D Human Mesh Reconstruction from a Single 2D Pose Based on Loop Structure.

Sensors (Basel, Switzerland)·2020

Related Experiment Video

Updated: Aug 25, 2025

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

891

ArbGaze: Gaze Estimation from Arbitrary-Sized Low-Resolution Images.

Hee Gyoon Kim1, Ju Yong Chang1

  • 1Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea.

Sensors (Basel, Switzerland)
|October 14, 2022
PubMed
Summary

This study introduces a novel gaze estimation method for low-resolution images. Combining knowledge distillation and feature adaptation significantly improves accuracy for arbitrary-sized images, enhancing real-world applications.

Keywords:
deep neural networkfeature adaptationgaze estimationknowledge distillation

More Related Videos

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.7K
Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
07:45

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

4.5K

Related Experiment Videos

Last Updated: Aug 25, 2025

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

891
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.7K
Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
07:45

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

4.5K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Gaze estimation aims to determine a gaze vector from facial images.
  • Current methods often struggle with varying image resolutions common in real-world scenarios.
  • Resolution variations degrade the performance of existing gaze estimation models.

Purpose of the Study:

  • To propose a robust gaze estimation method for arbitrary-sized, low-resolution images.
  • To address the performance degradation caused by resolution variations in in-the-wild images.
  • To improve the generalizability of gaze estimation techniques.

Main Methods:

  • A novel gaze estimation approach combining knowledge distillation and feature adaptation.
  • Knowledge distillation is used to generate feature maps comparable to high-resolution images.
  • Feature adaptation enables processing of diverse image resolutions by integrating low-resolution images with scale information.

Main Results:

  • The proposed method significantly improves gaze estimation performance in ablation studies.
  • Combining knowledge distillation and feature adaptation yields substantial performance gains.
  • The approach demonstrates effectiveness across various backbone architectures, indicating strong generalizability.

Conclusions:

  • The developed method effectively handles arbitrary-sized low-resolution images for gaze estimation.
  • The integration of knowledge distillation and feature adaptation offers a promising solution for real-world gaze analysis.
  • This technique enhances the robustness and applicability of gaze estimation models in diverse environments.