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

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,...

You might also read

Related Articles

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

Sort by

Same author

Efficacy and safety comparisons of PD-1 versus PD-L1 inhibitors in patients with solid cancer: a systematic review and meta-analysis.

Journal of the National Cancer Center·2026

Same author

Brown Adipocyte Sheets Alleviate Myocardial Ischemia-Reperfusion Injury Through NRG4-ErbB4-Dependent Ferroptosis Inhibition.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Pilot tests of continuous gas extraction with improved L-shaped wells from mining and goaf areas.

Scientific reports·2026

Same author

Federated Multi-View Unsupervised Feature Selection via Bio-Inspired Hierarchical-Cognitive Tianji's Horse Racing Optimization and Tensor Learning.

Biomimetics (Basel, Switzerland)·2026

Same author

A Post-Quantum Authentication and Key Agreement Protocol Based on Lattice-Based KEM for Secure Network Environments.

Entropy (Basel, Switzerland)·2026

Same author

Enzyme-responsive 612-AYR nanoparticles for targeted inhibition of B4GALT3-mediated ITGB1 glycosylation in hepatocellular carcinoma.

British journal of pharmacology·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Home
A Training-free Paradigm For Data-scarce Maritime Scene Classification Using Vision-language Models.

Home
A Training-free Paradigm For Data-scarce Maritime Scene Classification Using Vision-language Models.

Related Experiment Video

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

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

A Training-Free Paradigm for Data-Scarce Maritime Scene Classification Using Vision-Language Models.

Jiabao Wu¹, Yujie Chen¹, Wentao Chen¹

¹Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China.

Sensors (Basel, Switzerland)

|May 4, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel training-free method for Maritime Domain Awareness (MDA) using Large Vision-Language Models (VLMs). The approach enhances performance in data-scarce scenarios, outperforming traditional models with minimal data.

Keywords:

intelligent sensing networks maritime domain awareness optical spaceborne sensors sensor data processing training-free inference vision-language models

More Related Videos

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

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Related Experiment Videos

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

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

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

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Area of Science:

Remote Sensing
Artificial Intelligence
Computer Vision

Background:

Maritime Domain Awareness (MDA) heavily relies on high-resolution optical spaceborne sensor data.
Traditional supervised deep learning for MDA faces significant challenges due to the need for exhaustively annotated datasets.
Data scarcity severely degrades the performance of conventional models, hindering real-time applications.

Purpose of the Study:

To develop a training-free inference paradigm for MDA that overcomes data scarcity limitations.
To leverage the pre-trained knowledge of Large Vision-Language Models (VLMs) for enhanced MDA.
To bridge the perspective gap between natural images and top-down optical sensor imagery.

Main Methods:

Introduction of a Domain Knowledge-Enhanced In-Context Learning (DK-ICL) framework.

Coupling DK-ICL with a Macro-Topological Chain-of-Thought (MT-CoT) strategy.

Translating expert remote sensing heuristics into a step-by-step reasoning pipeline for VLMs.

Main Results:

MT-CoT augmented VLMs achieved over 38% higher F1-score compared to traditional models under data scarcity.
The zero-gradient approach demonstrated robust generalization on unannotated, out-of-distribution coastal clutters.
Performance parity was achieved with data-heavy networks using 50 times less data volume.

Conclusions:

The proposed training-free paradigm offers a resource-efficient foundation for next-generation intelligent maritime sensing networks.
This approach substitutes massive human annotation and GPU optimization with scalable logical deduction.
The framework significantly enhances MDA capabilities in data-limited environments.