PatFig: Generating Short and Long Captions for Patent Figures

ICCVW 2023 pp. 2835-2841

doi:10.1109/ICCVW60793.2023.00305 /iccvw/2023/aubakirova2023iccvw-patfig/

Abstract

This paper introduces Qatent PatFig, a novel large-scale patent figure dataset comprising 30,000+ patent figures from over 11,000 European patent applications. For each figure, this dataset provides short and long captions, reference numerals, their corresponding terms, and the minimal claim set that describes the interactions between the components of the image. To assess the usability of the dataset, we finetune an LVLM model on Qatent PatFig to generate short and long descriptions, and we investigate the effects of incorporating various text-based cues at the prediction stage of the patent figure captioning process.

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Text

Aubakirova et al. "PatFig: Generating Short and Long Captions for Patent Figures." IEEE/CVF International Conference on Computer Vision Workshops, 2023. doi:10.1109/ICCVW60793.2023.00305

Markdown

[Aubakirova et al. "PatFig: Generating Short and Long Captions for Patent Figures." IEEE/CVF International Conference on Computer Vision Workshops, 2023.](https://mlanthology.org/iccvw/2023/aubakirova2023iccvw-patfig/) doi:10.1109/ICCVW60793.2023.00305

BibTeX

@inproceedings{aubakirova2023iccvw-patfig,
  title     = {{PatFig: Generating Short and Long Captions for Patent Figures}},
  author    = {Aubakirova, Dana and Gerdes, Kim and Liu, Lufei},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2023},
  pages     = {2835-2841},
  doi       = {10.1109/ICCVW60793.2023.00305},
  url       = {https://mlanthology.org/iccvw/2023/aubakirova2023iccvw-patfig/}
}