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

Interpreting X̄ Charts01:13

Interpreting X̄ Charts

Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line represents the process mean,...
Pareto Chart00:52

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
The X̄ Chart00:58

The X̄ Chart

The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality characteristic in the order in which...
Pie Chart01:04

Pie Chart

A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
Interpreting R Charts01:22

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...
Bar Graph01:07

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...

You might also read

Related Articles

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

Sort by
Same author

VideoPASTA: 7K Preference Pairs That Matter for Video-LLM Alignment.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing·2026
Same author

ViDscribe: Multimodal AI for Customizing Audio Description and Question Answering in Online Videos.

Extended abstracts on Human factors in computing systems. CHI Conference·2026
Same author

DescribePro: Collaborative Audio Description with Human-AI Interaction.

ASSETS. Annual ACM Conference on Assistive Technologies·2026
Same author

OSCaR: Object State Captioning and State Change Representation.

Findings of ACL. NAACL·2025
Same author

Mutual Masking and Perceptual Simultaneity in Electrical Muscle Stimulation and Vibration Haptics.

IEEE transactions on haptics·2025
Same author

VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition·2025
Same journal

PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2026
Same journal

Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2026
Same journal

ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2025
Same journal

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2025
Same journal

MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2025
Same journal

CUNSB-RFIE: Context-aware Unpaired Neural Schrödinger Bridge in Retinal Fundus Image Enhancement.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2025
See all related articles

Related Experiment Video

Updated: May 15, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

ChartQA-X: Generating Explanations for Visual Chart Reasoning.

Shamanthak Hegde1, Pooyan Fazli1, Hasti Seifi1

  • 1Arizona State University.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision
|May 14, 2026
PubMed
Summary
This summary is machine-generated.

We introduce ChartQA-X, a dataset for generating chart explanations and answers. Models trained on this data significantly improve explanation quality and question-answering accuracy for complex visual data.

Related Experiment Videos

Last Updated: May 15, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Area of Science:

  • Artificial Intelligence
  • Data Visualization
  • Natural Language Processing

Background:

  • Effective data-driven decision-making relies on understanding complex chart information.
  • Generating accurate and coherent explanations for charts remains a significant challenge.

Purpose of the Study:

  • To develop a comprehensive dataset, ChartQA-X, for training models to generate detailed explanations for chart images.
  • To evaluate the quality of model-generated explanations against human-written ones.

Main Methods:

  • Created ChartQA-X dataset with 30,799 chart samples, questions, answers, and explanations across four chart types.
  • Generated and selected explanations based on faithfulness, informativeness, coherence, and perplexity metrics.
  • Conducted human evaluations with 245 participants to assess explanation quality.

Main Results:

  • Model-generated explanations in ChartQA-X outperformed human explanations in accuracy and logic.
  • Fine-tuning models on ChartQA-X led to significant improvements: up to 24.57 points in explanation quality and 18.96 points in question-answering accuracy.
  • Models demonstrated strong performance on unseen benchmarks, with a 14.75 percentage point gain.

Conclusions:

  • ChartQA-X dataset facilitates the development of advanced AI agents capable of explaining complex visual data.
  • The integrated approach of providing explanations alongside answers enhances comprehension and user trust in AI responses.