Visualizations themselves have become a data format. Akin to other data formats such as
text and images, visualizations are increasingly created, stored, shared, and (re-) used with …

Generative AI (GenAI) has witnessed remarkable progress in recent years and
demonstrated impressive performance in various generation tasks in different domains such …

Deep multi-view clustering utilizes neural networks to extract the potential peculiarities of
complementarity and consistency information among multi-view features. This can obtain a …

Extracting underlying data from rasterized charts is tedious and inaccurate; values might be
partially occluded or hard to distinguish, and the quality of the image limits the precision of …

Unsupervised learning algorithms (eg, self-supervised learning, auto-encoder, contrastive
learning) allow deep learning models to learn effective image representations from large …

Data charts are prevalent across various fields due to their efficacy in conveying complex
data relationships. However, static charts may sometimes struggle to engage readers and …

Automated chart analysis has vast potential to improve the accessibility of charts for a wider
audience, eg, people with visual impairments or other disabilities, by generating captions for …

We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and
diverse NL datasets using Vega-Lite specifications as input, thereby streamlining the …

Finding line-chart images similar to a given line-chart image query is a common task in data
exploration and image query systems, eg finding similar trends in stock markets or medical …

This paper presents a complete review of different approaches across all components of the
chart image detection and classification up to date. A set of 89 scientific papers is collected …