@inproceedings{liu-etal-2022-artist,
    title = "{ARTIST}: A Transformer-based {C}hinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge",
    author = "Liu, Tingting  and
      Wang, Chengyu  and
      Zhu, Xiangru  and
      Li, Lei  and
      Qiu, Minghui  and
      Huang, Jun  and
      Gao, Ming  and
      Xiao, Yanghua",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2022.findings-emnlp.62/",
    doi = "10.18653/v1/2022.findings-emnlp.62",
    pages = "881--888",
    abstract = "Text-to-Image Synthesis (TIS) is a popular task to convert natural language texts into realistic images. Recently, transformer-based TIS models (such as DALL-E) have been proposed using the encoder-decoder architectures. Yet, these billion-scale TIS models are difficult to tune and deploy in resource-constrained environments. In addition, there is a lack of language-specific TIS benchmarks for Chinese, together with high-performing models with moderate sizes. In this work, we present ARTIST, A tRansformer-based Chinese Text-to-Image SynThesizer for high-resolution image generation. In ARTIST, the rich linguistic and relational knowledge facts are injected into the model to ensure better model performance without the usage of ultra-large models. We further establish a large-scale Chinese TIS benchmark with the re-production results of state-of-the-art transformer-based TIS models.Results show ARTIST outperforms previous approaches."
}