Self-Ensembling Vision-Language Models for Chart Data Extraction 事件

Self-Ensembling Vision-Language Models for Chart Data Extraction arXiv:2605.27298v1 Announce Type: new Abstract: Charts effectively convey quantitative information, but the underlying data are often locked in image form, hindering reuse and analysis. Manually digitizing charts is time-consuming and error-prone, motivating automatic chart-to-table extraction. Recent approaches use specialized vision-language models (VLMs), yet performance still lags on charts with many datapoints or substantial