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

Association Areas of the Cortex01:21

Association Areas of the Cortex

10.2K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
10.2K

You might also read

Related Articles

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

Sort by
Same author

Targeted α-synuclein mRNA degradation by PMO-based RNA-degrading chimeras.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Age differences in proactive and reactive control: valence asymmetries in the processing of complex emotional scenes.

BMC psychology·2026
Same author

LGR5 as a therapeutic target for decidualization-based endometrial mesenchymal stem cells therapy in thin endometrium.

Stem cell research & therapy·2026
Same author

Depleting prion protein using splice-switching small molecules.

bioRxiv : the preprint server for biology·2026
Same author

Relative Deprivation and Moral Disengagement as Serial Mediators Between Cyberbullying Victimization and Psychological Distress Symptoms Among Victim-Only Five-Year Higher Vocational College Students.

Behavioral sciences (Basel, Switzerland)·2026
Same author

Characterizing the molecular signature of obesity through urine metabolomics in a large Chinese population.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences·2026

Related Experiment Video

Updated: May 2, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K

IRFNet: Cognitive-Inspired Iterative Refinement Fusion Network for Camouflaged Object Detection.

Guohan Li1,2, Jingxin Wang1,3, Jianming Wei1

  • 1Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
Summary

This study introduces the Iterative Refinement Fusion Network (IRFNet) for camouflaged object detection (COD). IRFNet enhances feature representation and iteratively refines detection, outperforming existing methods on benchmark datasets.

Keywords:
attention mechanismcamouflaged object detectioncomputer visioncross-level feature fusioniterative refinementobject detection

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.7K
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

448

Related Experiment Videos

Last Updated: May 2, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.7K
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

448

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Camouflaged Object Detection (COD) identifies objects hidden by blending with surroundings.
  • Existing methods struggle with extreme object-background similarity, hindering feature capture and detail preservation.

Purpose of the Study:

  • To develop a novel framework, the Iterative Refinement Fusion Network (IRFNet), for improved camouflaged object detection.
  • To address limitations in capturing discriminative features and modeling multiscale patterns in COD.

Main Methods:

  • Proposed IRFNet mimics human visual cognition using progressive feature enhancement and iterative optimization.
  • Incorporated a Hierarchical Feature Enhancement Module (HFEM) with dynamic attention for multiscale feature enrichment.
  • Utilized a Context-guided Iterative Optimization Framework (CIOF) with transformer-based global context and dual-branch supervision.

Main Results:

  • IRFNet demonstrated superior performance against fourteen state-of-the-art methods on CAMO, COD10K, and NC4K datasets.
  • Achieved performance improvements ranging from 0.9% to 13.7% across key detection metrics.
  • Ablation studies confirmed the effectiveness of individual components and the iterative refinement strategy.

Conclusions:

  • IRFNet effectively addresses challenges in camouflaged object detection posed by object-background similarity.
  • The proposed framework offers a significant advancement in accurately detecting concealed objects.
  • Iterative refinement is a key strategy for progressively enhancing detection accuracy in COD.