PGDP5K: A Diagram Parsing Dataset for Plane Geometry Problems

• URL: http://arxiv.org/abs/2205.09947v1
• Date: Fri, 20 May 2022 03:41:41 GMT
• Title: PGDP5K: A Diagram Parsing Dataset for Plane Geometry Problems
• Authors: Yihan Hao (1 and 2), Mingliang Zhang (2 and 3), Fei Yin (2 and 3) and Linlin Huang (1) ((1) Beijing Jiaotong University, (2) Institute of Automation of Chinese Academy of Science, (3) University of Chinese Academy of Sciences)
• Abstract summary: We propose a new large-scale geometry diagram dataset named PGDP5K and a novel annotation method. Our dataset consists of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types. Experiments on PGDP5K and IMP-Geometry3K datasets reveal that the state-of-the-art (SOTA) method achieves only 66.07% F1 value.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diagram parsing is an important foundation for geometry problem solving, attracting increasing attention in the field of intelligent education and document image understanding. Due to the complex layout and between-primitive relationship, plane geometry diagram parsing (PGDP) is still a challenging task deserving further research and exploration. An appropriate dataset is critical for the research of PGDP. Although some datasets with rough annotations have been proposed to solve geometric problems, they are either small in scale or not publicly available. The rough annotations also make them not very useful. Thus, we propose a new large-scale geometry diagram dataset named PGDP5K and a novel annotation method. Our dataset consists of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types. Different from previous datasets, our PGDP5K dataset is labeled with more fine-grained annotations at primitive level, including primitive classes, locations and relationships. What is more, combined with above annotations and geometric prior knowledge, it can generate intelligible geometric propositions automatically and uniquely. We performed experiments on PGDP5K and IMP-Geometry3K datasets reveal that the state-of-the-art (SOTA) method achieves only 66.07% F1 value. This shows that PGDP5K presents a challenge for future research. Our dataset is available at http://www.nlpr.ia.ac.cn/databases/CASIA-PGDP5K/.

Related papers

• A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications [67.33002207179923]
  This paper presents a survey of data structures, models, and applications related to geometric GNNs. We provide a unified view of existing models from the geometric message passing perspective. We also summarize the applications as well as the related datasets to facilitate later research for methodology development and experimental evaluation.
  arXiv  Detail & Related papers  (2024-03-01T12:13:04Z)
• Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images [56.86175251327466]
  We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints. Our method unifies depth and surface normal estimations within a cohesive framework, which enables the generation of high-quality 3D geometry from images.
  arXiv  Detail & Related papers  (2024-02-08T17:57:59Z)
• G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model [124.68242155098189]
  Large language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities. G-LLaVA demonstrates exceptional performance in solving geometric problems, significantly outperforming GPT-4-V on the MathVista benchmark with only 7B parameters.
  arXiv  Detail & Related papers  (2023-12-18T17:36:20Z)
• Improving embedding of graphs with missing data by soft manifolds [51.425411400683565]
  The reliability of graph embeddings depends on how much the geometry of the continuous space matches the graph structure. We introduce a new class of manifold, named soft manifold, that can solve this situation. Using soft manifold for graph embedding, we can provide continuous spaces to pursue any task in data analysis over complex datasets.
  arXiv  Detail & Related papers  (2023-11-29T12:48:33Z)
• Exploring Data Geometry for Continual Learning [64.4358878435983]
  We study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space.
  arXiv  Detail & Related papers  (2023-04-08T06:35:25Z)
• A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram [33.62866585222121]
  We propose a new neural solver called PGPSNet to fuse multi-modal information efficiently. PGPSNet is endowed with rich knowledge of geometry theorems and geometric representation. We build a new large-scale and fine-annotated GPS dataset named PGPS9K.
  arXiv  Detail & Related papers  (2023-02-22T02:38:25Z)
• Study of Manifold Geometry using Multiscale Non-Negative Kernel Graphs [32.40622753355266]
  We propose a framework to study the geometric structure of the data. We make use of our recently introduced non-negative kernel (NNK) regression graphs to estimate the point density, intrinsic dimension, and the linearity of the data manifold (curvature)
  arXiv  Detail & Related papers  (2022-10-31T17:01:17Z)
• Plane Geometry Diagram Parsing [29.921409628478152]
  We propose a powerful diagram based on deep learning and graph reasoning. A modified instance segmentation method is proposed to extract geometric primitives. The graph neural network (GNN) is leveraged to realize relation parsing and primitive classification.
  arXiv  Detail & Related papers  (2022-05-19T07:47:01Z)
• Algebraic Machine Learning with an Application to Chemistry [0.0]
  We develop a machine learning pipeline that captures fine-grain geometric information without relying on smoothness assumptions. In particular, we propose a for numerically detecting points lying near the singular locus of the underlying variety.
  arXiv  Detail & Related papers  (2022-05-11T22:41:19Z)
• GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning [172.36214872466707]
  We focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge. We propose a Geometric Question Answering dataset GeoQA, containing 5,010 geometric problems with corresponding annotated programs.
  arXiv  Detail & Related papers  (2021-05-30T12:34:17Z)

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.