README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - zh
license:
  - cc-by-sa-4.0
multilinguality: []
size_categories: []
source_datasets:
  - extended|PosterErase
task_categories:
  - other
task_ids: []
pretty_name: PKU-PosterLayout
tags:
  - layout-generation
  - graphic design
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: original_poster
      dtype: image
    - name: inpainted_poster
      dtype: image
    - name: basnet_saliency_map
      dtype: image
    - name: pfpn_saliency_map
      dtype: image
    - name: canvas
      dtype: image
    - name: annotations
      sequence:
        - name: poster_path
          dtype: string
        - name: total_elem
          dtype: int32
        - name: cls_elem
          dtype: int32
        - name: box_elem
          sequence: int32
  splits:
    - name: train
      num_bytes: 7145995465.374
      num_examples: 9974
    - name: test
      num_bytes: 366746518
      num_examples: 905
  download_size: 7351207315
  dataset_size: 7512741983.374

Dataset Card for PKU-PosterLayout

Table of Contents

• Dataset Card Creation Guide
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Supported Tasks and Leaderboards
    • Languages
  • Dataset Structure
    • Data Instances
    • Data Fields
    • Data Splits
  • Dataset Creation
    • Curation Rationale
    • Source Data
      • Initial Data Collection and Normalization
      • Who are the source language producers?
    • Annotations
      • Annotation process
      • Who are the annotators?
    • Personal and Sensitive Information
  • Considerations for Using the Data
    • Social Impact of Dataset
    • Discussion of Biases
    • Other Known Limitations
  • Additional Information
    • Dataset Curators
    • Licensing Information
    • Citation Information
    • Contributions

Dataset Description

• Homepage: http://59.108.48.34/tiki/PosterLayout/
• Repository: https://github.com/shunk031/huggingface-datasets_PKU-PosterLayout
• Paper (Preprint): https://arxiv.org/abs/2303.15937
• Paper (CVPR2023): https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_PosterLayout_A_New_Benchmark_and_Approach_for_Content-Aware_Visual-Textual_Presentation_CVPR_2023_paper.html

Dataset Summary

PKU-PosterLayout is a new dataset and benchmark for content-aware visual-textual presentation layout.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

The language data in PKU-PosterLayout is in Chinese (BCP-47 zh).

Dataset Structure

Data Instances

To use PKU-PosterLayout dataset, you need to download the poster image and saliency maps via PKU Netdisk or Google Drive.

/path/to/datasets
├── train
│   ├── inpainted_poster.zip
│   ├── original_poster.zip
│   ├── saliencymaps_basnet.zip
│   └── saliencymaps_pfpn.zip
└── test
    ├── image_canvas.zip
    ├── saliencymaps_basnet.zip
    └── saliencymaps_pfpn.zip

import datasets as ds

dataset = ds.load_dataset(
    path="shunk031/PKU-PosterLayout",
    data_dir="/path/to/datasets/",
)

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

[More Information Needed]

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

[More Information Needed]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@inproceedings{hsu2023posterlayout,
  title={PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout},
  author={Hsu, Hsiao Yuan and He, Xiangteng and Peng, Yuxin and Kong, Hao and Zhang, Qing},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6018--6026},
  year={2023}
}

Contributions

Thanks to @PKU-ICST-MIPL for creating this dataset.