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Building cross-model intelligence that can understand charts and communicatethe salient information hidden behind them is an appealing challenge in thevision and language(V+L) community. The capability to uncover the underlinedtable data of chart figures is a critical key to automatic chart understanding.We introduce ChartT5, a V+L model that learns how to interpret tableinformation from chart images via cross-modal pre-training on plot table pairs.Specifically, we propose two novel pre-training objectives: Masked HeaderPrediction (MHP) and Masked Value Prediction (MVP) to facilitate the model withdifferent skills to interpret the table information. We have conductedextensive experiments on chart question answering and chart summarization toverify the effectiveness of the proposed pre-training strategies. Inparticular, on the ChartQA benchmark, our ChartT5 outperforms thestate-of-the-art non-pretraining methods by over 8% performance gains.

Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairs