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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Masking and Demasking Agents01:19

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on the metal...
Association Areas of the Cortex01:21

Association Areas of the Cortex

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,...

You might also read

Related Articles

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

Sort by
Same author

Independent and interactive effects of personal light exposure and air pollution on incident COPD: A prospective cohort study with 13 million hours of light sensor data.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data.

Machine intelligence research (Beijing)·2026
Same author

An IGF2BP3-dependent metabolic circuit governs macrophage recruitment and immunosuppression in glioblastoma.

Cell reports·2026
Same author

Q-Bone system: an intelligent quantitative system for alveolar bone loss to assist the diagnosis of periodontitis - model development and validation.

Journal of translational medicine·2026
Same author

Astaxanthin ameliorates cardiac damage induced by prepubertal di(2-ethylhexyl) phthalate exposure via inhibition of PPAR-α-mediated excessive autophagy.

Journal of environmental sciences (China)·2026
Same author

RBFOX2 suppresses NETosis and glioma growth via 5hmC-dependent PDGFB decay.

Cell reports·2026

Related Experiment Video

Updated: Jun 27, 2026

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

CamoFormer: Masked Separable Attention for Camouflaged Object Detection.

Bowen Yin, Xuying Zhang, Deng-Ping Fan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 5, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Identifying camouflaged objects is difficult. A new method, CamoFormer, uses masked separable attention (MSA) to improve camouflaged object detection and segmentation accuracy, achieving state-of-the-art results.

    More Related Videos

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    7.6K
    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

    499

    Related Experiment Videos

    Last Updated: Jun 27, 2026

    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
    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    7.6K
    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

    499

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Object detection and segmentation are challenging, especially for camouflaged targets.
    • Existing methods struggle to accurately distinguish camouflaged objects from complex backgrounds.

    Purpose of the Study:

    • To develop a novel model for enhanced camouflaged object detection and segmentation.
    • To introduce a new attention mechanism for improved feature representation.

    Main Methods:

    • A new model, CamoFormer, was developed using a backbone encoder and a top-down decoder.
    • The core innovation is the masked separable attention (MSA) mechanism, inspired by Transformers.
    • MSA utilizes three distinct mask strategies to differentiate camouflaged objects from backgrounds.

    Main Results:

    • CamoFormer achieved state-of-the-art performance on three benchmark datasets for camouflaged object detection.
    • The model demonstrated significant improvements in segmentation accuracy, particularly around object borders.
    • New metrics, BR-M and BR-F, were proposed to evaluate performance in border regions.

    Conclusions:

    • CamoFormer offers a significant advancement in camouflaged object detection and segmentation.
    • The proposed masked separable attention mechanism is effective for challenging visual tasks.
    • The new evaluation metrics provide a more comprehensive assessment of model performance.