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

Comprehensive analysis of the multi-rings mitochondrial genome of Populus tomentosa.

BMC genomics·2025
Same author

Evaluation of Cold Resistance in Alfalfa Varieties Based on Root Traits and Winter Survival in Horqin Sandy Land.

Biology·2025
Same author

Tryptophan metabolism-related gene CYP1B1 serves as a shared biomarker for both Parkinson's disease and insomnia.

Scientific reports·2025
Same author

A study of specific immunoglobulin G4 expression in allergic rhinitis and its value in assessing efficacy and in predicting prognosis of sublingual immunotherapy.

The Kaohsiung journal of medical sciences·2024
Same author

Non-affinity platform for processing knob-into-hole bispecific antibody.

Bioresources and bioprocessing·2024
Same author

High Relative Humidity-Induced Growth of Perovskite Nanowires from Glass toward Single-Mode Photonic Nanolasers at Sub-100-nm Scale.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2024

Related Experiment Video

Updated: Jun 9, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

An One-step Triple Enhanced weakly supervised semantic segmentation using image-level labels.

Longjie Quan1, Dandan Huang1, Zhi Liu1,2

  • 1School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun, People's Republic of China.

Plos One
|October 21, 2024
PubMed
Summary

This study introduces a One-Step Triple Enhanced (OSTE) network for weakly supervised semantic segmentation, simplifying models and improving accuracy using image-level labels. OSTE achieves better results than existing methods by enhancing localization and refining segmentation boundaries.

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
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

475

Related Experiment Videos

Last Updated: Jun 9, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
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

475

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional semantic segmentation requires pixel-level labels, which are time-consuming and resource-intensive to acquire.
  • Weakly supervised semantic segmentation uses image-level labels, reducing annotation costs but often lacking precise localization information.
  • Existing weakly supervised methods often employ complex two-step approaches, increasing model parameters and structural complexity.

Purpose of the Study:

  • To propose an innovative One-Step Triple Enhanced (OSTE) weakly supervised semantic segmentation network.
  • To streamline the model structure by integrating pseudo-label generation and segmentation into a single step.
  • To enhance segmentation accuracy by improving localization and boundary refinement using image-level labels.

Main Methods:

  • Developed the OSTE network, a one-step approach for pseudo-label generation and semantic segmentation.
  • Integrated local activation map information with images to improve localization and expansion capabilities.
  • Refined class activation map seed regions by exploiting multi-level feature correlations.
  • Incorporated conditional random field theory for generating high-confidence pseudo-labels with rich boundary details.

Main Results:

  • The OSTE network achieved a competitive mean Intersection over Union (mIoU) score of 58.47% on the Pascal VOC dataset.
  • Demonstrated significant improvements over prevailing two-step weakly supervised semantic segmentation schemes.
  • Outperformed existing end-to-end schemes by at least 5.03% in mIoU score.

Conclusions:

  • The proposed OSTE network offers a more streamlined and effective approach to weakly supervised semantic segmentation.
  • The triple enhancement strategy significantly boosts segmentation accuracy, particularly in localization and boundary definition.
  • OSTE presents a competitive alternative to existing methods, reducing complexity while achieving superior performance.