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  3. Vol. 9, No. 4, November 2024
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Vol. 9, No. 4, November 2024

Issue Published : Nov 1, 2024
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Layout Generation: Automated Components Placement for Advertising Poster using Transformer-based from Layout Graph

https://doi.org/10.22219/kinetik.v9i4.2035
Aisyah Dliya Ramadhanti
Telkom University
Kemas Rahmat Saleh Wiharja
Telkom University
Azmi Nurzakiah
Digital BRIBRAIN
Yoga Yustiawan
Digital BRIBRAIN

Corresponding Author(s) : Kemas Rahmat Saleh Wiharja

bagindokemas@telkomuniversity.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 9, No. 4, November 2024
Article Published : Nov 1, 2024

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Abstract

In the digital era, graphic design plays an important role in a company's marketing strategy, especially advertising posters that can convey messages to the audience. However, the process of creating attractive and informative posters takes a long time, especially the component placement on the layout. This research aims to develop a layout generator system that automatically places components on the layout using one of the transformer-based models. The transformer-based model used is a Graph Transformer with edge features called SGTransformer, which accepts input data as a graph. SGTransformer consists of several graph transformer layers that will calculate the attention of node and edge features on the input layout graph. A layout graph describes the spatial relationship between components in a layout. The SGTransformer model was trained by using advertising poster datasets collected from social media. The performance of the model were evaluated using the evaluation metrics commonly used in the layout generation domain such as Alignment, Overlap, Max IoU, and FID. The scores obtained from each evaluation metric are 0.025, 1.274, 0.325, and 8.575 respectively. The model evaluation results show that SGTransformer can produce structured and more diverse layouts although there are still challenges such as overlap between components.  Code and other materials will be released at https://github.com/syahdeee/Layout-Generator.

Keywords

Layout Generation Transformer-based Model Layout Graph Advertising Poster Automated Components Positioning
Ramadhanti, A. D., Wiharja, K. R. S., Nurzakiah, A., & Yustiawan, Y. (2024). Layout Generation: Automated Components Placement for Advertising Poster using Transformer-based from Layout Graph. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 9(4). https://doi.org/10.22219/kinetik.v9i4.2035
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References
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  29. K. Kikuchi, E. Simo-Serra, M. Otani, and K. Yamaguchi, “Constrained Graphic Layout Generation via Latent Optimization,” in MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia, Association for Computing Machinery, Inc, Oct. 2021, pp. 88–96. https://doi.org/10.1145/3474085.3475497
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References


E. Setiawan Nababan, “Implementation of Advertising Poster As A Promotional Media For MSME.”

K. J. Murchie and D. Diomede, “Fundamentals of Graphic Design-essential tools for effective visual science communication,” Facets, vol. 5, no. 1. Canadian Science Publishing, pp. 409–422, Jun. 11, 2020. https://doi.org/10.1139/facets-2018-0049

J. Li, J. Yang, A. Hertzmann, J. Zhang, and T. Xu, “LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators,” Jan. 2019. https://doi.org/10.48550/arXiv.1901.06767

A. A. Jyothi, T. Durand, J. He, L. Sigal, and G. Mori, “LayoutVAE: Stochastic Scene Layout Generation From a Label Set,” Jul. 2019. https://doi.org/10.48550/arXiv.1907.10719

X. Zheng, X. Qiao, Y. Cao, and R. W. H. Lau, “Content-aware generative modeling of graphic design layouts,” ACM Trans Graph, vol. 38, no. 4, Jul. 2019. https://doi.org/10.1145/3306346.3322971

J. Li, J. Yang, A. Hertzmann, J. Zhang, and T. Xu, “LayoutGAN: Synthesizing Graphic Layouts with Vector-Wireframe Adversarial Networks,” IEEE Trans Pattern Anal Mach Intell, vol. 43, no. 7, pp. 2388–2399, Jul. 2021. https://doi.org/10.1109/TPAMI.2019.2963663

H.-Y. Lee et al., “Neural Design Network: Graphic Layout Generation with Constraints,” Dec. 2019. https://doi.org/10.48550/arXiv.1912.09421

S. Chai, L. Zhuang, and F. Yan, “LayoutDM: Transformer-based Diffusion Model for Layout Generation,” May 2023. https://doi.org/10.48550/arXiv.2305.02567

D. M. Arroyo, J. Postels, and F. Tombari, “Variational Transformer Networks for Layout Generation.”. https://doi.org/10.48550/arXiv.2104.02416

J. Johnson, A. Gupta, and L. Fei-Fei, “Image Generation from Scene Graphs,” Apr. 2018. https://doi.org/10.48550/arXiv.1804.01622

R. Sortino, S. Palazzo, and C. Spampinato, “Transformer-based Image Generation from Scene Graphs,” Mar. 2023. https://doi.org/10.48550/arXiv.2303.04634

E. Quiring, A. Müller, and K. Rieck, “On the Detection of Image-Scaling Attacks in Machine Learning,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Dec. 2023, pp. 506–520. https://doi.org/10.1145/3627106.3627134

“Automated Catalog Generation using Deep Learning,” International Research Journal of Modernization in Engineering Technology and Science, Aug. 2023. https://doi.org/10.56726/irjmets44010

P. S. Parsania and P. V Virparia, “International Journal on Recent and Innovation Trends in Computing and Communication Performance Analysis of Image Scaling Algorithms”. https://doi.org/10.17762/ijritcc.v4i6.2359

O. I. Khalaf, C. A. T. Romero, A. Azhagu Jaisudhan Pazhani, and G. Vinuja, “VLSI Implementation of a High-Performance Nonlinear Image Scaling Algorithm,” J Healthc Eng, vol. 2021, 2021. https://doi.org/10.1155/2021/6297856

E. Quiring and K. Rieck, “Backdooring and Poisoning Neural Networks with Image-Scaling Attacks,” Mar. 2020. https://doi.org/10.48550/arXiv.2003.08633

J. Shi, S. Sun, Z. Shi, C. Zheng, and B. She, “Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm,” Sustainability (Switzerland), vol. 14, no. 22, Nov. 2022. https://doi.org/10.3390/su142215329

G. Zhu et al., “Scene Graph Generation: A Comprehensive Survey,” Jan. 2022. https://doi.org/10.48550/arXiv.2201.00443

S. Khandelwal and L. Sigal, “Iterative Scene Graph Generation,” Jul. 2022. https://doi.org/10.48550/arXiv.2207.13440

M. Wang et al., “Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks,” Sep. 2019. https://doi.org/10.48550/arXiv.1909.01315

V. P. Dwivedi and X. Bresson, “A Generalization of Transformer Networks to Graphs,” Dec. 2020. https://doi.org/10.48550/arXiv.2012.09699

Z. Chen et al., “PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments,” Jul. 2020. https://doi.org/10.48550/arXiv.2007.09584

Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, “Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression,” Nov. 2019. https://doi.org/10.48550/arXiv.1911.08287

I. Ullah, M. Manzo, M. Shah, and M. Madden, “Graph Convolutional Networks: analysis, improvements and results,” Dec. 2019. https://doi.org/10.48550/arXiv.1912.09592

A. Sobolevsky, G.-A. Bilodeau, J. Cheng, and J. L. C. Guo, “GUILGET: GUI Layout GEneration with Transformer,” Apr. 2023. https://doi.org/10.48550/arXiv.2304.09012

J. Li, J. Yang, J. Zhang, C. Liu, C. Wang, and T. Xu, “Attribute-conditioned Layout GAN for Automatic Graphic Design,” Sep. 2020. https://doi.org/10.48550/arXiv.2009.05284

R. Carletto, H. Cardot, and N. Ragot, “Deep Learning for Document Layout Generation: A First Reproducible Quantitative Evaluation and a Baseline Model,” pp. 20–35, 2021. https://doi.org/10.1007/978-3-030-86334-0_2

Q. Jing et al., “Layout Generation for Various Scenarios in Mobile Shopping Applications,” in Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, Apr. 2023. https://doi.org/10.1145/3544548.3581446

K. Kikuchi, E. Simo-Serra, M. Otani, and K. Yamaguchi, “Constrained Graphic Layout Generation via Latent Optimization,” in MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia, Association for Computing Machinery, Inc, Oct. 2021, pp. 88–96. https://doi.org/10.1145/3474085.3475497

E. Min et al., “Transformer for Graphs: An Overview from Architecture Perspective,” Feb. 2022. https://doi.org/10.48550/arXiv.2202.08455

S. Yun, M. Jeong, R. Kim, J. Kang, and H. J. Kim, “Graph Transformer Networks,” Nov. 2019. https://doi.org/10.48550/arXiv.1911.06455

Author Biographies

Azmi Nurzakiah, Digital BRIBRAIN

Digital Banking Development and Operation Division, Bank Rakyat Indonesia (BRI)

Yoga Yustiawan, Digital BRIBRAIN

Digital Banking Development and Operation Division, Bank Rakyat Indonesia (BRI)

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KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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