ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language models (VLMs) remain limited. We introduce ChartNet, a high-quality, million-scale multimodal dataset designed to advance chart interpretation and reasoning. ChartNet leverages a novel code-guided synthesis pipeline to generate 1.5 million diverse chart samples spanning 24 chart types and 6 plotting libraries. Each sample consists of five aligned components: plotting code, rendered chart image, data table, natural language summary, and question-answering with reasoning, providing fine-grained cross-modal alignm...
| Authors | Jovana Kondic, Pengyuan Li, Dhiraj Joshi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 March 2026 |
| Last updated at source | 14 April 2026 |
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