@inproceedings{zhang-etal-2026-recontraster,
title = "{R}e{C}ontraster: Making Your Posters Stand Out with Regional Contrast",
author = "Zhang, Peixuan and
Jia, Zijian and
Cai, Ziqi and
Weng, Shuchen and
Li, Si and
Shi, Boxin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.98/",
pages = "2155--2171",
ISBN = "979-8-89176-390-6",
abstract = "Effective poster design requires rapidly capturing attention and clearly conveying messages. Inspired by the ``contrast effects'' principle, we propose ReContraster, the first training-free model to leverage regional contrast to make posters stand out. By emulating the cognitive behaviors of a poster designer, ReContraster introduces the compositional multi-agent system to identify elements, organize layout, and evaluate generated poster candidates. To further ensure harmonious transitions across region boundaries, ReContraster integrates the hybrid denoising strategy during the diffusion process. We additionally contribute a new benchmark dataset for comprehensive evaluation. Seven quantitative metrics and four user studies confirm its superiority over relevant state-of-the-art methods, producing visually striking and aesthetically appealing posters."
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<abstract>Effective poster design requires rapidly capturing attention and clearly conveying messages. Inspired by the “contrast effects” principle, we propose ReContraster, the first training-free model to leverage regional contrast to make posters stand out. By emulating the cognitive behaviors of a poster designer, ReContraster introduces the compositional multi-agent system to identify elements, organize layout, and evaluate generated poster candidates. To further ensure harmonious transitions across region boundaries, ReContraster integrates the hybrid denoising strategy during the diffusion process. We additionally contribute a new benchmark dataset for comprehensive evaluation. Seven quantitative metrics and four user studies confirm its superiority over relevant state-of-the-art methods, producing visually striking and aesthetically appealing posters.</abstract>
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%0 Conference Proceedings
%T ReContraster: Making Your Posters Stand Out with Regional Contrast
%A Zhang, Peixuan
%A Jia, Zijian
%A Cai, Ziqi
%A Weng, Shuchen
%A Li, Si
%A Shi, Boxin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhang-etal-2026-recontraster
%X Effective poster design requires rapidly capturing attention and clearly conveying messages. Inspired by the “contrast effects” principle, we propose ReContraster, the first training-free model to leverage regional contrast to make posters stand out. By emulating the cognitive behaviors of a poster designer, ReContraster introduces the compositional multi-agent system to identify elements, organize layout, and evaluate generated poster candidates. To further ensure harmonious transitions across region boundaries, ReContraster integrates the hybrid denoising strategy during the diffusion process. We additionally contribute a new benchmark dataset for comprehensive evaluation. Seven quantitative metrics and four user studies confirm its superiority over relevant state-of-the-art methods, producing visually striking and aesthetically appealing posters.
%U https://aclanthology.org/2026.acl-long.98/
%P 2155-2171
Markdown (Informal)
[ReContraster: Making Your Posters Stand Out with Regional Contrast](https://aclanthology.org/2026.acl-long.98/) (Zhang et al., ACL 2026)
ACL
- Peixuan Zhang, Zijian Jia, Ziqi Cai, Shuchen Weng, Si Li, and Boxin Shi. 2026. ReContraster: Making Your Posters Stand Out with Regional Contrast. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2155–2171, San Diego, California, United States. Association for Computational Linguistics.