Language-agnostic Semantic Consistent Text-to-Image Generation

SeongJun Jung, Woo Suk Choi, Seongho Choi, Byoung-Tak Zhang


Abstract
Recent GAN-based text-to-image generation models have advanced that they can generate photo-realistic images matching semantically with descriptions. However, research on multi-lingual text-to-image generation has not been carried out yet much. There are two problems when constructing a multilingual text-to-image generation model: 1) language imbalance issue in text-to-image paired datasets and 2) generating images that have the same meaning but are semantically inconsistent with each other in texts expressed in different languages. To this end, we propose a Language-agnostic Semantic Consistent Generative Adversarial Network (LaSC-GAN) for text-to-image generation, which can generate semantically consistent images via language-agnostic text encoder and Siamese mechanism. Experiments on relatively low-resource language text-image datasets show that the model has comparable generation quality as images generated by high-resource language text, and generates semantically consistent images for texts with the same meaning even in different languages.
Anthology ID:
2022.mml-1.1
Volume:
Proceedings of the Workshop on Multilingual Multimodal Learning
Month:
May
Year:
2022
Address:
Dublin, Ireland and Online
Venue:
MML
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–5
Language:
URL:
https://aclanthology.org/2022.mml-1.1
DOI:
10.18653/v1/2022.mml-1.1
Bibkey:
Cite (ACL):
SeongJun Jung, Woo Suk Choi, Seongho Choi, and Byoung-Tak Zhang. 2022. Language-agnostic Semantic Consistent Text-to-Image Generation. In Proceedings of the Workshop on Multilingual Multimodal Learning, pages 1–5, Dublin, Ireland and Online. Association for Computational Linguistics.
Cite (Informal):
Language-agnostic Semantic Consistent Text-to-Image Generation (Jung et al., MML 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.mml-1.1.pdf
Video:
 https://aclanthology.org/2022.mml-1.1.mp4
Data
COCOCOCO-CN