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

Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

122
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
122
Responses to Drought and Flooding02:41

Responses to Drought and Flooding

10.8K
Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
10.8K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

52
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
52
Deconvolution01:20

Deconvolution

212
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...
212
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.1K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
7.1K
Histogram01:05

Histogram

14.0K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
14.0K

You might also read

Related Articles

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

Sort by
Same author

A 3D-printed self-propelled, highly sensitive mini-motor for underwater pesticide detection.

Talanta·2018
Same author

Integrated analysis of microarray data to identify the genes critical for the rupture of intracranial aneurysm.

Oncology letters·2018
Same author

Editorial: Recent Advances of Cell and Gene Therapy in Kidney Diseases.

Current gene therapy·2018
Same author

Photocatalytic Supramolecular Enantiodifferentiating Dimerization of 2-Anthracenecarboxylic Acid through Triplet-Triplet Annihilation.

Organic letters·2018
Same author

On the realization of acoustic attenuation using a microperforated panel alone.

The Journal of the Acoustical Society of America·2018
Same author

Advances in covalent organic frameworks in separation science.

Journal of chromatography. A·2018
Same journal

Supporting human-agent communication for explainable planning in spatial-temporal planning problems.

Neural computing & applications·2026
Same journal

Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition.

Neural computing & applications·2026
Same journal

Sequential pattern transformer (SPT): a generative and interpretable framework for predicting disease trajectories.

Neural computing & applications·2026
Same journal

Balancing misclassification errors in image-based inference using problem domain semantics and a nested cascade architecture.

Neural computing & applications·2025
Same journal

Deep multi-objective reinforcement learning for utility-based infrastructural maintenance optimization.

Neural computing & applications·2025
Same journal

A fairness scale for real-time recidivism forecasts using a national database of convicted offenders.

Neural computing & applications·2025
See all related articles

Related Experiment Video

Updated: Aug 2, 2025

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

592

Spatial-aware topic-driven-based image Chinese caption for disaster news.

Jinfei Zhou1, Yaping Zhu1, Yana Zhang1

  • 1State Key Laboratory of Media Convergence and Communication, The Communication University of China, Beijing, 100024 China.

Neural Computing & Applications
|April 20, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method for automatically describing disaster news images. This approach uses a spatial-aware topic-driven caption network (STCNet) trained on a large dataset to generate accurate and informative captions, improving disaster communication.

Keywords:
Dataset of disaster newsGaussian mixture modelImage captionNews topic classification

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

644

Related Experiment Videos

Last Updated: Aug 2, 2025

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

592
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

644

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Automatic image captioning is crucial for disseminating disaster information.
  • Existing models struggle to incorporate essential news elements into disaster image descriptions.
  • A need exists for specialized datasets and models for disaster news image analysis.

Purpose of the Study:

  • To create a large-scale Chinese dataset for disaster news image captioning (DNICC19k).
  • To propose a novel Spatial-aware Topic driven Caption network (STCNet) for generating descriptive disaster news captions.
  • To evaluate STCNet's performance against benchmark models.

Main Methods:

  • Developed the DNICC19k dataset with annotated disaster news images.
  • Proposed STCNet, a network that uses graph representations and topic distributions.
  • Incorporated a graph reasoning module with spatial information and a learnable Gaussian kernel.
  • Utilized spatial-aware graph representations and topic distributions to drive sentence generation.

Main Results:

  • STCNet successfully generates descriptive sentences related to news topics for disaster images.
  • The model achieves superior performance on multiple evaluation metrics compared to benchmark models.
  • Achieved CIDEr and BLEU-4 scores of 60.26 and 17.01, respectively.

Conclusions:

  • The developed STCNet model effectively captions disaster news images.
  • The DNICC19k dataset facilitates advancements in disaster image understanding.
  • This research enhances automated disaster reporting and communication.