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PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout

This repository contains the guidelines of benchmark PKU PosterLayout and Pytorch implementation of DS-GAN for "PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout", CVPR 2023.

For dataset details and downloads, please visit our project page.

How to Run

Prerequisites

If your operating system is linux-64, directly run

conda create --name yourenvname --file spec-file.txt

Otherwise, try

pip install -r requirements.txt

• Environment

Python 3.9
CUDA 11.0

• Module

torch==1.12.1
torchvision==0.13.1
timm==0.6.5
opencv-python==4.6.0.66
pandas==1.4.3
Pillow==9.2.0

Models

1. Download pre-trained weights from PKU Netdisk(pw: P04X) or Google Drive
2. Put corresponding .pth files under model_weight/ or output/, as follow:

model_weight/
├─ resnet18-5c106cde.pth
├─ resnet50_a1_0-14fe96d1.pth
output/
├─ DS-GAN-Epoch300.pth

Dataset

1. Download PKU PosterLayout from the project page
2. Unzip compressed files to corresponding directories
3. Put directories under Dataset/, as follow:

Dataset/
├─ train/
│  ├─ inpainted_poster/
│  ├─ saliencymaps_basnet/
│  ├─ saliencymaps_pfpn/
├─ test/
│  ├─ image_canvas/
│  ├─ saliencymaps_basnet/
│  ├─ saliencymaps_pfpn/
├─ train_csv_9973.csv

Usage

• Training

sh train.sh

• Testing and Evaluating

sh test_n_eval.sh

Citation

If our work is helpful for your research, please cite our paper:

@inproceedings{Hsu-2023-posterlayout,
    title={PosterLayout: A New Benchmark and  Approach for Content-Aware Visual-Textual Presentation Layout},
    author={HsiaoYuan Hsu, Xiangteng He, Yuxin Peng, Hao Kong and Qing Zhang},
    booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2023},
    pages={6018-6026}
}

Contact us

For any questions or further information, please email Ms. Hsu (kslh99@stu.pku.edu.cn).