VProChart: Answering Chart Question through Visual Perception Alignment Agent and Programmatic Solution Reasoning

• URL: http://arxiv.org/abs/2409.01667v1
• Date: Tue, 3 Sep 2024 07:19:49 GMT
• Title: VProChart: Answering Chart Question through Visual Perception Alignment Agent and Programmatic Solution Reasoning
• Authors: Muye Huang, Lingling Zhang, Lai Han, Wenjun Wu, Xinyu Zhang, Jun Liu,
• Abstract summary: VProChart is a novel framework designed to address the challenges of Chart Question Answering (CQA) It integrates a lightweight Visual Perception Alignment Agent (VPAgent) and a Programmatic Solution Reasoning approach. VProChart significantly outperforms existing methods, highlighting its capability in understanding and reasoning with charts.
• Score: 13.011899331656018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Charts are widely used for data visualization across various fields, including education, research, and business. Chart Question Answering (CQA) is an emerging task focused on the automatic interpretation and reasoning of data presented in charts. However, chart images are inherently difficult to interpret, and chart-related questions often involve complex logical and numerical reasoning, which hinders the performance of existing models. This paper introduces VProChart, a novel framework designed to address these challenges in CQA by integrating a lightweight Visual Perception Alignment Agent (VPAgent) and a Programmatic Solution Reasoning approach. VPAgent aligns and models chart elements based on principles of human visual perception, enhancing the understanding of chart context. The Programmatic Solution Reasoning approach leverages large language models (LLMs) to transform natural language reasoning questions into structured solution programs, facilitating precise numerical and logical reasoning. Extensive experiments on benchmark datasets such as ChartQA and PlotQA demonstrate that VProChart significantly outperforms existing methods, highlighting its capability in understanding and reasoning with charts.

Related papers

• MSG-Chart: Multimodal Scene Graph for ChartQA [11.828192162922436]
  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.
  arXiv  Detail & Related papers  (2024-08-09T04:11:23Z)
• 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)
• On Pre-training of Multimodal Language Models Customized for Chart Understanding [83.99377088129282]
  This paper explores the training processes necessary to improve MLLMs' comprehension of charts. We introduce CHOPINLLM, an MLLM tailored for in-depth chart comprehension.
  arXiv  Detail & Related papers  (2024-07-19T17:58:36Z)
• ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild [28.643565008567172]
  We introduce ChartGemma, a novel chart understanding and reasoning model developed over PaliGemma. Rather than relying on underlying data tables, ChartGemma is trained on instruction-tuning data generated directly from chart images. Our simple approach achieves state-of-the-art results across $5$ benchmarks spanning chart summarization, question answering, and fact-checking.
  arXiv  Detail & Related papers  (2024-07-04T22:16:40Z)
• 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)
• 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: Perception, Structuring, Reasoning for Visual Chart Understanding [58.38480335579541]
  Current chart-related tasks focus on either chart perception which refers to extracting information from the visual charts, or performing reasoning given the extracted data. In this paper, we aim to establish a unified and label-efficient learning paradigm for joint perception and reasoning tasks. Experiments are conducted on various chart-related tasks, demonstrating the effectiveness and promising potential for a unified chart perception-reasoning paradigm.
  arXiv  Detail & Related papers  (2023-09-20T12:51:13Z)
• RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic [8.155575318208628]
  We introduce a benchmark and dataset for chart visual QA on real-world charts. Our contribution includes the introduction of a new answer type, 'list', with both ranked and unranked variations. Results of our experiments, conducted on a real-world out-of-distribution dataset, provide a robust evaluation of large-scale pre-trained models.
  arXiv  Detail & Related papers  (2023-08-03T18:21:38Z)
• Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairs [71.55796212450055]
  We introduce ChartT5, a V+L model that learns how to interpret table information from chart images via cross-modal pre-training on plot table pairs. Specifically, we propose two novel pre-training objectives: Masked Header Prediction (MHP) and Masked Value Prediction (MVP)
  arXiv  Detail & Related papers  (2023-05-29T22:29:03Z)
• UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning [29.947053208614246]
  We present UniChart, a pretrained model for chart comprehension and reasoning. UniChart encodes the relevant text, data, and visual elements of charts and then uses a chart-grounded text decoder to generate the expected output in natural language. We propose several chart-specific pretraining tasks that include: (i) low-level tasks to extract the visual elements (e.g., bars, lines) and data from charts, and (ii) high-level tasks to acquire chart understanding and reasoning skills.
  arXiv  Detail & Related papers  (2023-05-24T06:11:17Z)
• 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.