MSG-Chart: Multimodal Scene Graph for ChartQA

Autor:	Dai, Yue, Han, Soyeon Caren, Liu, Wei
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
DOI:	10.1145/3627673.3679967
Popis:	Automatic Chart Question Answering (ChartQA) is challenging due to the complex distribution of chart elements with patterns of the underlying data not explicitly displayed in charts. To address this challenge, we design a joint multimodal scene graph for charts to explicitly represent the relationships between chart elements and their patterns. Our proposed multimodal scene graph includes a visual graph and a textual graph to jointly capture the structural and semantical knowledge from the chart. This graph module can be easily integrated with different vision transformers as inductive bias. Our experiments demonstrate that incorporating the proposed graph module enhances the understanding of charts' elements' structure and semantics, thereby improving performance on publicly available benchmarks, ChartQA and OpenCQA. Comment: Accpeted by CIKM Short 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.04852 Zobrazit plný text záznamu View this record from Arxiv