[2603.27064] ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.27064 (cs) [Submitted on 28 Mar 2026] Title:ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding Authors:Jovana Kondic, Pengyuan Li, Dhiraj Joshi, Isaac Sanchez, Ben Wiesel, Shafiq Abedin, Amit Alfassy, Eli Schwartz, Daniel Caraballo, Yagmur Gizem Cinar, Florian Scheidegger, Steven I. Ross, Daniel Karl I. Weidele, Hang Hua, Ekaterina Arutyunova, Roei Herzig, Zexue He, Zihan Wang, Xinyue Yu, Yunfei Zhao, Sicong Jiang, Minghao Liu, Qunshu Lin, Peter Staar, Luis Lastras, Aude Oliva, Rogerio Feris View a PDF of the paper titled ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding, by Jovana Kondic and Pengyuan Li and Dhiraj Joshi and Isaac Sanchez and Ben Wiesel and Shafiq Abedin and Amit Alfassy and Eli Schwartz and Daniel Caraballo and Yagmur Gizem Cinar and Florian Scheidegger and Steven I. Ross and Daniel Karl I. Weidele and Hang Hua and Ekaterina Arutyunova and Roei Herzig and Zexue He and Zihan Wang and Xinyue Yu and Yunfei Zhao and Sicong Jiang and Minghao Liu and Qunshu Lin and Peter Staar and Luis Lastras and Aude Oliva and Rogerio Feris View PDF HTML (experimental) Abstract: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, millio...