  Chart comprehension presents significant challenges for machine learning
models due to the diverse and intricate shapes of charts. Existing multimodal
methods often overlook these visual features or fail to integrate them
effectively for chart question answering (ChartQA). To address this, we
introduce Chartformer, a unified framework that enhances chart component
recognition by accurately identifying and classifying components such as bars,
lines, pies, titles, legends, and axes. Additionally, we propose a novel
Question-guided Deformable Co-Attention (QDCAt) mechanism, which fuses chart
features encoded by Chartformer with the given question, leveraging the
question's guidance to ground the correct answer. Extensive experiments
demonstrate that the proposed approaches significantly outperform baseline
models in chart component recognition and ChartQA tasks, achieving improvements
of 3.2% in mAP and 15.4% in accuracy, respectively. These results underscore
the robustness of our solution for detailed visual data interpretation across
various applications.
