← 研踪 PaperTrailCVF · CVPR 2026:cvf:05225fedd64e7ffd30dc382bbe0db107 · 记录 #1
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
Kondic, JovanaLi, PengyuanJoshi, DhirajSanchez, IsaacWiesel, BenAbedin, ShafiqAlfassy, AmitSchwartz, EliCaraballo, DanielCinar, Yagmur GizemScheidegger, FlorianRoss, Steven I.Weidele, Daniel Karl I.Hua, HangArutyunova, EkaterinaHerzig, RoeiWang, ZihanYu, XinyueZhao, YunfeiJiang, SicongLiu, MinghaoLin, QunshuOliva, AudeFeris, Rogerio 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 04:21
摘要
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 alignment. To capture the full spectrum of chart comprehension, ChartNet additionally includes specialized subsets encompassing human annotated data, real-world data, safety, and grounding. Moreover, a rigorous quality-filtering pipeline ensures visual fidelity, semantic accuracy, and diversity across chart representations. Fine-tuning on ChartNet consistently improves results across benchmarks, demonstrating its utility as large-scale supervision for multimodal models. As the largest open-source dataset of its kind, ChartNet aims to support the development of foundation models with robust and generalizable capabilities for data visualization understanding. The dataset is publicly available at https://huggingface.co/datasets/ibm-granite/ChartNet
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记录 #1 · 2026 · 最新观测版本
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 alignment. To capture the full spectrum of chart comprehension, ChartNet additionally includes specialized subsets encompassing human annotated data, real-world data, safety, and grounding. Moreover, a rigorous quality-filtering pipeline ensures visual fidelity, semantic accuracy, and diversity across chart representations. Fine-tuning on ChartNet consistently improves results across benchmarks, demonstrating its utility as large-scale supervision for multimodal models. As the largest open-source dataset of its kind, ChartNet aims to support the development of foundation models with robust and generalizable capabilities for data visualization understanding. The dataset is publicly available at https://huggingface.co/datasets/ibm-granite/ChartNet
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