metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - en
license: gpl-3.0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - multiple-choice
  - question-answering
  - visual-question-answering
task_ids:
  - multiple-choice-qa
  - visual-question-answering
  - multi-class-classification
tags:
  - multi-modal-qa
  - figure-qa
  - vqa
  - scientific-figure
  - geometry-diagram
  - chart
  - chemistry
dataset_info:
  features:
    - name: image_path
      dtype: string
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: prompt_reasoning
      dtype: string
    - name: prompt_no_reasoning
      dtype: string
    - name: image_category
      dtype: string
    - name: task_category
      dtype: string
    - name: question_type
      dtype: string
    - name: response_options
      sequence: string
    - name: source
      dtype: string
    - name: id
      dtype: string
    - name: decoded_image
      dtype: image
  splits:
    - name: geometry__triangle
      num_bytes: 242889
      num_examples: 50
    - name: geometry__quadrilateral
      num_bytes: 210787
      num_examples: 50
    - name: geometry__length
      num_bytes: 271748
      num_examples: 50
    - name: geometry__angle
      num_bytes: 255692
      num_examples: 50
  download_size: 607689
  dataset_size: 981116
configs:
  - config_name: default
    data_files:
      - split: geometry__triangle
        path: data/geometry__triangle-*
      - split: geometry__quadrilateral
        path: data/geometry__quadrilateral-*
      - split: geometry__length
        path: data/geometry__length-*
      - split: geometry__angle
        path: data/geometry__angle-*

VisOnlyQA

This repository contains the code and data for the paper "VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information".

VisOnlyQA is designed to evaluate the visual perception capability of large vision language models (LVLMs) on geometric information of scientific figures. The evaluation set includes 1,200 mlutiple choice questions in 12 visual perception tasks on 4 categories of scientific figures. We also provide a training dataset consisting of 70k instances.

Datasets:
- Eval-Real: https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Real
- Eval-Synthetic: https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Synthetic
- Train: https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Train
Code: https://github.com/psunlpgroup/VisOnlyQA

@misc{kamoi2024visonlyqa,
    title={VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information}, 
    author={Ryo Kamoi and Yusen Zhang and Sarkar Snigdha Sarathi Das and Ranran Haoran Zhang and Rui Zhang},
    year={2024},
}

Dataset

The dataset is provided in Hugging Face Dataset.

Eval-Real: https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Real
- 500 instances for questions on figures in existing datasets (e.g., MathVista, MMMU, and CharXiv)
Eval-Synthetic: https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Synthetic
- 700 instances for questions on synthetic figures
Train: https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Train
- 70,000 instances for training (synthetic figures)

dataset folder of the GitHub repository includes identical datasets, except for the training data.

Examples

Usage

from datasets import load_dataset

real_eval = load_dataset("ryokamoi/VisOnlyQA_Eval_Real")
real_synthetic = load_dataset("ryokamoi/VisOnlyQA_Eval_Synthetic")

# Splits
print(real_eval.keys())
# dict_keys(['geometry__triangle', 'geometry__quadrilateral', 'geometry__length', 'geometry__angle', 'geometry__area', 'geometry__diameter_radius', 'chemistry__shape_single', 'chemistry__shape_multi', 'charts__extraction', 'charts__intersection'])

print(real_synthetic.keys())
# dict_keys(['syntheticgeometry__triangle', 'syntheticgeometry__quadrilateral', 'syntheticgeometry__length', 'syntheticgeometry__angle', 'syntheticgeometry__area', '3d__size', '3d__angle'])

# Prompt
print(real_eval['geometry__triangle'][0]['prompt_no_reasoning'])
# There is no triangle ADP in the figure. True or False?

# A triangle is a polygon with three edges and three vertices, which are explicitly connected in the figure.

# Your response should only include the final answer (True, False). Do not include any reasoning or explanation in your response.

# Image
print(real_eval['geometry__triangle'][0]['decoded_image'])
# <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=103x165 at 0x7FB4F83236A0>

# Answer
print(real_eval['geometry__triangle'][0]['answer'])
# False

Data Format

Each instance of VisOnlyQA dataset has the following attributes:

Features

decoded_image: [PIL.Image] Input image
question: [string] Question (without instruction)
prompt_reasoning: [string] Prompt with intstruction to use chain-of-thought
prompt_no_reasoning: [string] Prompt with intstruction not to use chain-of-thought
answer: [string] Correct answer (e.g., True, a)

Metadata

image_path: [string] Path to the image file
image_category: [string] Category of the image (e.g., geometry, chemistry)
question_type: [string] single_answer or multiple answers
task_category: [string] Category of the task (e.g., triangle)
response_options: [List[string]] Multiple choice options (e.g., ['True', 'False'], ['a', 'b', 'c', 'd', 'e'])
source: [string] Source dataset
id: [string] Unique ID

Statistics

License

Please refer to LICENSE.md.

Contact

If you have any questions, feel free to open an issue or reach out directly to Ryo Kamoi (ryokamoi@psu.edu).