Poster Generation Dataset
Collection
4 items
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Updated
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original_poster
image | inpainted_poster
image | canvas
image | basnet_saliency_map
image | pfpn_saliency_map
image | annotations
sequence |
|---|---|---|---|---|---|
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Not supported with pagination yet | {
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Not supported with pagination yet | {
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Not supported with pagination yet | {
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End of preview.
Dataset Card for PKU-PosterLayout
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
import datasets as ds
dataset = ds.load_dataset("creative-graphic-design/PKU-PosterLayout")
# or you can download RALF (https://arxiv.org/abs/2311.13602)-style preprocessed dataset
dataset = ds.load_dataset("creative-graphic-design/PKU-PosterLayout", name="ralf-style")
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.
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Homepage:
59.108.48.34
Repository:
github.com
Paper (Preprint):
arxiv.org
Paper (CVPR2023):
openaccess.thecvf.com