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About this paper
Renqiu Xia, Haoyang Peng, Hancheng Ye, Mingsheng Li, Xiangchao Yan, Peng Ye, Botian Shi, Yu Qiao and 2 more
StructChart unifies visual chart perception and reasoning by introducing Structured Triplet Representations (STR) for explicit data encoding, a comprehensive evaluation metric (SCRM) for perception, and an LLM-based data augmentation scheme (SimChart9K). This framework enhances zero/few-shot learning and achieves superior performance on chart question answering, notably outperforming GPT-4V on the ChartQA benchmark.
Key insights
- Explicitly structuring chart information into (Entity, Entity, Value) triplets provides a more robust and interpretable intermediate representation, significantly improving the performance of downstream reasoning tasks compared to linearized data.
- A dedicated, multi-faceted metric capable of evaluating both the structural integrity and numerical precision of extracted chart data is essential for accurate assessment and advancement of chart perception models.
- Large Language Models can be effectively leveraged to synthesize high-quality, diverse chart datasets, dramatically reducing the reliance on costly human annotations and enabling strong zero/few-shot learning capabilities for visual chart understanding.
Summary by alphaXiv.
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