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

Vision01:24

Vision

59.3K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
59.3K
Force Classification01:22

Force Classification

2.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.3K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.8K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.8K
Deconvolution01:20

Deconvolution

543
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
543

You might also read

Related Articles

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

Sort by
Same author

Evaluating the Therapeutic Potential of <i>Hyriopsis cumingii</i> Polysaccharide in Osteoarthritis: Insights from a Mouse Model.

Rejuvenation research·2026
Same author

Correction: The PurR family transcriptional regulator promotes butenyl-spinosyn production in Saccharopolyspora pogona.

Applied microbiology and biotechnology·2026
Same author

Giant rapidly involuting congenital hemangioma in a neonate: a case report.

Frontiers in pediatrics·2026
Same author

Fetal decapitation with live birth in the first trimester: a rare case report.

Journal of medical ultrasonics (2001)·2026
Same author

Development and validation of a noninvasive machine learning model using urinary extracellular vesicle physical parameters for prostate cancer diagnosis.

Scientific reports·2026
Same author

Evolving classifiers with background suppression transformer for open-set long-tailed class-incremental remote sensing scene classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Jan 15, 2026

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

3.3K

Unbiased Semantic Decoding With Vision Foundation Models for Few-Shot Segmentation.

Jin Wang, Bingfeng Zhang, Jian Pang

    IEEE Transactions on Neural Networks and Learning Systems
    |October 6, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an unbiased semantic decoding strategy for few-shot segmentation using the Segment Anything Model (SAM) and Contrastive Language-Image Pretraining (CLIP). The method enhances SAM

    More Related Videos

    Automated Analysis of C. elegans Fluorescence Images using SegElegans
    06:27

    Automated Analysis of C. elegans Fluorescence Images using SegElegans

    Published on: October 10, 2025

    584
    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

    9.6K

    Related Experiment Videos

    Last Updated: Jan 15, 2026

    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

    3.3K
    Automated Analysis of C. elegans Fluorescence Images using SegElegans
    06:27

    Automated Analysis of C. elegans Fluorescence Images using SegElegans

    Published on: October 10, 2025

    584
    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

    9.6K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Few-shot segmentation (FSS) aims to segment objects with limited labeled data.
    • Recent methods leverage the Segment Anything Model (SAM) for FSS due to its generalization capabilities.
    • Existing SAM-based FSS approaches suffer from biased decoding due to reliance on support set prompts.

    Purpose of the Study:

    • To propose an unbiased semantic decoding (USD) strategy for few-shot segmentation.
    • To enhance SAM's generalization for unknown classes by integrating Contrastive Language-Image Pretraining (CLIP) semantics.
    • To improve the accuracy and reduce bias in SAM's decoding process for FSS.

    Main Methods:

    • Developed an unbiased semantic decoding (USD) strategy integrating SAM and CLIP.
    • Implemented feature enhancement strategies: global image-level and local pixel-level guidance.
    • Introduced a learnable visual-text target prompt generator (VTPG) using CLIP features.
    • Ensured no retraining of foundation vision models.

    Main Results:

    • The proposed USD strategy significantly improves SAM's performance in few-shot segmentation.
    • Feature enhancement strategies effectively leverage CLIP for semantic alignment and target localization.
    • The VTPG generates informative prompts for better target discrimination.
    • Achieved new state-of-the-art results on PASCAL-$5^i$ and COCO-$20^i$ datasets.

    Conclusions:

    • The USD strategy effectively overcomes the limitations of prompt-biased decoding in SAM-based FSS.
    • Integrating CLIP semantics enhances SAM's ability to adapt to unknown classes.
    • The method offers a robust and high-performing solution for few-shot segmentation without extensive retraining.