Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Structural Classification of Joints

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Force Classification

Force Classification

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,...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

You might also read

Related Articles

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

Sort by

Same author

MoS<sub>2</sub> quantum dot-decorated CNT networks as a sulfur host for enhanced electrochemical kinetics in advanced lithium-sulfur batteries.

Nanoscale advances·2024

Same author

Template assisted preparation of silicone (polydimethylsiloxane) elastomers and their self-cleaning application.

RSC advances·2022

Same author

An Efficient Sampling-Based Attention Network for Semantic Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022

Same author

Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking.

Sensors (Basel, Switzerland)·2021

Same author

Question-Guided Erasing-Based Spatiotemporal Attention Learning for Video Question Answering.

IEEE transactions on neural networks and learning systems·2021

Same author

Extremely Lightweight Skeleton-Based Action Recognition With ShiftGCN+.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021

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

Search research articles

Related Experiment Video

Updated: May 24, 2025

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

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

Published on: November 30, 2022

Hierarchical Contrastive Learning for Semantic Segmentation.

Jie Jiang, Xingjian He, Weining Wang

IEEE Transactions on Neural Networks and Learning Systems

|March 3, 2025

Summary

This summary is machine-generated.

This study introduces hierarchical contrastive learning (Hi-CL) for semantic segmentation, enhancing feature representation by exploring multi-scale pixel-to-component relationships. Hi-CL improves model performance and achieves state-of-the-art results on benchmark datasets.

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: May 24, 2025

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

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

Published on: November 30, 2022

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

Single-scale contrastive learning in semantic segmentation aims for unified pixel representations.
Limitations include overly extreme representations and restricted receptive fields, hindering category feature reflection.

Purpose of the Study:

To extend single-scale feature space to multi-scale for improved semantic segmentation.
To propose a hierarchical contrastive learning (Hi-CL) method exploring pixel-to-component semantic relationships.

Main Methods:

Generating multi-scale candidate samples using pooling windows of varying sizes on feature maps.
Pruning the sample set using threshold-based criteria for effective feature representation learning.
Applying Hi-CL to learn pixel-to-component consistency with selected samples.

Main Results:

The proposed Hi-CL method demonstrates consistent improvement when applied to existing semantic segmentation models.
Achieved state-of-the-art results on Cityscapes, ADE20K, and COCO Stuff benchmarks.
Hi-CL effectively addresses limitations of single-scale approaches by incorporating multi-scale information.

Conclusions:

Hierarchical contrastive learning offers a robust approach to enhance semantic segmentation.
The method is easily integrated into current semantic segmentation frameworks.
Hi-CL advances the state-of-the-art in semantic segmentation through improved feature representation.