Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts
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 fine-grained perception results in the omission of critical visual cues; (2) redundant or noisy visual information undermines the performance of multimodal reasoning; (3) lack of adaptive deep reasoning relative to the amount of visual information. To tackle these challenges, we present a novel focus-driven fine-grained chart reasoning model, Chart-FR1, to improve perception, focusing efficien...
| Authors | Hongkun Pan, Yuwei Wu, Wanyi Hong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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