MSG-Chart: Multimodal Scene Graph for ChartQA

• URL: http://arxiv.org/abs/2408.04852v1
• Date: Fri, 9 Aug 2024 04:11:23 GMT
• Title: MSG-Chart: Multimodal Scene Graph for ChartQA
• Authors: Yue Dai, Soyeon Caren Han, Wei Liu,
• Abstract summary: 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. 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.
• Score: 11.828192162922436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Multimodal Graph Constrastive Learning and Prompt for ChartQA [11.828192162922436]
  ChartQA presents significant challenges due to the complex distribution of chart elements and the implicit patterns embedded within the underlying data. We have developed a joint multimodal scene graph for charts, explicitly representing the relationships between chart elements and their associated patterns.
  arXiv  Detail & Related papers  (2025-01-08T06:27:07Z)
• InstructG2I: Synthesizing Images from Multimodal Attributed Graphs [50.852150521561676]
  We propose a graph context-conditioned diffusion model called InstructG2I. InstructG2I first exploits the graph structure and multimodal information to conduct informative neighbor sampling. A Graph-QFormer encoder adaptively encodes the graph nodes into an auxiliary set of graph prompts to guide the denoising process.
  arXiv  Detail & Related papers  (2024-10-09T17:56:15Z)
• Advancing Chart Question Answering with Robust Chart Component Recognition [18.207819321127182]
  We introduce a unified framework that enhances chart component recognition by accurately identifying and classifying components such as bars, lines, pies, titles, legends, and axes. We also propose a novel Question-guided Deformable Co-Attention mechanism, which fuses chart features encoded by Chartformer with the given question, leveraging the question's guidance to ground the correct answer.
  arXiv  Detail & Related papers  (2024-07-19T20:55:06Z)
• TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning [83.58521787193293]
  We present TinyChart, an efficient MLLM for chart understanding with only 3B parameters. TinyChart overcomes two key challenges in efficient chart understanding: (1) reduce the burden of learning numerical computations through a Program-of-Thoughts (PoT) learning strategy, and (2) reduce lengthy vision feature sequences produced by the vision transformer for high-resolution images through a Vision Token Merging module.
  arXiv  Detail & Related papers  (2024-04-25T14:23:24Z)
• ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning [54.82612435284695]
  We benchmark the ability of off-the-shelf Multi-modal Large Language Models (MLLMs) in the chart domain. We construct ChartX, a multi-modal evaluation set covering 18 chart types, 7 chart tasks, 22 disciplinary topics, and high-quality chart data. We develop ChartVLM to offer a new perspective on handling multi-modal tasks that strongly depend on interpretable patterns.
  arXiv  Detail & Related papers  (2024-02-19T14:48:23Z)
• ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning [54.89249749894061]
  ChartAssistant is a vision-language model for universal chart comprehension and reasoning. It undergoes a two-stage training process, starting with pre-training on chart-to-table parsing to align chart and text. Experimental results demonstrate significant performance gains over the state-of-the-art UniChart and Chartllama method.
  arXiv  Detail & Related papers  (2024-01-04T17:51:48Z)
• StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding [54.45681512355684]
  Current chart-related tasks focus on either chart perception that extracts information from the visual charts, or chart reasoning given the extracted data. We introduce StructChart, a novel framework that leverages Structured Triplet Representations (STR) to achieve a unified and label-efficient approach.
  arXiv  Detail & Related papers  (2023-09-20T12:51:13Z)
• ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules [89.75395046894809]
  We present ChartReader, a unified framework that seamlessly integrates chart derendering and comprehension tasks. Our approach includes a transformer-based chart component detection module and an extended pre-trained vision-language model for chart-to-X tasks. Our proposed framework can significantly reduce the manual effort involved in chart analysis, providing a step towards a universal chart understanding model.
  arXiv  Detail & Related papers  (2023-04-05T00:25:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.