Hongkun Pan — accepted AI-conference papers

2026

Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts

Hongkun Pan, Yuwei Wu, Wanyi Hong, Shenghui Hu, Qitong Yan, Yi Yang +5

CVPR 2026

Multimodal large language models (MLLMs) have shown considerable potential in chart understanding and reasoning tasks. However, they still struggle with high information density (HID) charts characterized by multiple subplots, legends, and dense annotations due to three major challenges: (1) limited

Cited by 0 Source ↗code ↗Scholar ↗

2025

R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

Yi Yang, Xiaoxuan He, Hongkun Pan, Xiyan Jiang, Yan Deng, Xingtao Yang +6

ICCV 2025poster

Large Language Models have demonstrated remarkable reasoning capability in complex textual tasks. However, multimodal reasoning, which requires integrating visual and textual information, remains a significant challenge. Existing visual-language models often struggle to effectively analyze and reaso…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗