Datasets:
language:
- en
license: cc-by-nc-4.0
size_categories:
- 10K<n<100K
task_categories:
- image-to-text
- visual-question-answering
- image-classification
pretty_name: PatFig
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: image_name
dtype: string
- name: pub_number
dtype: string
- name: title
dtype: string
- name: figs_norm
sequence: string
- name: short_description
sequence: string
- name: long_description
sequence: string
- name: short_description_token_count
dtype: int64
- name: long_description_token_count
dtype: int64
- name: draft_class
dtype: string
- name: cpc_class
dtype: string
- name: relevant_terms
list:
- name: element_identifier
dtype: string
- name: terms
sequence: string
- name: associated_claims
dtype: string
- name: compound
dtype: bool
- name: references
sequence: string
splits:
- name: train
num_bytes: 1998632864.066
num_examples: 17386
- name: test
num_bytes: 118291788
num_examples: 998
download_size: 1735361199
dataset_size: 2116924652.066
PatFig Dataset
![PatFig Dataset Logo](https://cdn-lfs-us-1.huggingface.co/repos/25/0c/250cb7eb9b83b2bd76ad6440700971baf0ec2981fdcb94b7fad768f2eb59fecc/1e79b8cf6cbe22d424c95e4816fc763e388d80cba4228908d707100f1f41182a?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27patfig_funny_logo.png%3B+filename%3D%22patfig_funny_logo.png%22%3B&response-content-type=image%2Fpng&Expires=1710484025&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDQ4NDAyNX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzI1LzBjLzI1MGNiN2ViOWI4M2IyYmQ3NmFkNjQ0MDcwMDk3MWJhZjBlYzI5ODFmZGNiOTRiN2ZhZDc2OGYyZWI1OWZlY2MvMWU3OWI4Y2Y2Y2JlMjJkNDI0Yzk1ZTQ4MTZmYzc2M2UzODhkODBjYmE0MjI4OTA4ZDcwNzEwMGYxZjQxMTgyYT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=ghxVaJztNO%7EJsQTPLCGf5VjyqxZQBbQEwpEFUlg9jp0pwu6YySncyKudoNVEJkRJlXpIi6pTw0ezQ4VpMHa4BrirgO8JfmxzVJQ5h1wgq9KtRwpdYt0ba%7EH73KNCSS1KvNM50prZ5fKBon3s9yvL1wgkOgIMN2i7NPaR1MCzps8sMyhbWFpwVpvuoV91QS20OGwGsWmYW3IQ3cEsCczadefAI%7EehWswamDxG3UZ%7ErYrMqXprFlpiB1fTas7SPeKpDT4V5YdTtEHUZRQ14Lb0QEogydoNmgj4afvqrqwV-6dnL3Z7iaJxDrdxYTIk6VfPIHIH1%7E7dyLjRwG8gdhvhCQ__&Key-Pair-Id=KCD77M1F0VK2B)
Table of Contents
Introduction
The PatFig Dataset is a curated collection of over 18,000 patent images from more than 7,000 European patent applications, spanning the year 2020. It aims to provide a comprehensive resource for research and applications in image captioning, abstract reasoning, patent analysis, and automated documentprocessing. The overarching goal of this dataset is to advance the research in visually situated language understanding towards more hollistic consumption of the visual and textual data.
Dataset Description
Overview
This dataset includes patent figures accompanied by short and long captions, reference numerals, corresponding terms, and a minimal set of claims, offering a detailed insight into the depicted inventions.
Structure
- Image Files: Technical drawings, block diagrams, flowcharts, plots, and grayscale photographs.
- Captions: Each figure is accompanied by a short and long caption describing its content and context.
- Reference Numerals and Terms: Key components in the figures are linked to their descriptions through reference numerals.
- Minimal Set of Claims: Claims sentences summarizing the interactions among elements within each figure.
- Metadata: Includes image names, publication numbers, titles, figure identifiers, and more. The detailed descriptions of the fields are available in the Dataset Documentation.
Categories
The dataset is categorized according to the International Patent Classification (IPC) system, ensuring a diverse representation of technological domains.
Usage
The PatFig Dataset is intended for use in patent image analysis, document image processing, visual question answering tasks, and image captioning in technical contexts. Users are encouraged to explore innovative applications in related fields.
Challenges and Considerations
Users should be aware of challenges such as interpreting compound figures. PatFig was built automatically using high-performance machine-learning and deep-learning methods. Therefore, the data might contain noise, which was mentioned in the corresponding paper.
License and Usage Guidelines
The dataset is released under a Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0) License. It is intended for non-commercial use, and users must adhere to the license terms.
Cite as
@inproceedings{aubakirova2023patfig,
title={PatFig: Generating Short and Long Captions for Patent Figures},
author={Aubakirova, Dana and Gerdes, Kim and Liu, Lufei},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={2843--2849},
year={2023}
}