Phrase-Grounding-Aware Supervised Fine-Tuning for Chart Recognition via Side-Masked Attention
Recent advances in chart recognition have been driven by supervised fine-tuning (SFT) of vision-language models (VLMs), which unify multiple related tasks, and by diversifying training corpora. In parallel, research on leveraging large language models (LLMs) for object detection has shown that joint