Prompt Refinement with Image Pivot for Text-to-Image Generation
<|reference_start|>Prompt Refinement with Image Pivot for Text-to-Image Generation: For text-to-image generation, automatically refining user-provided natural language prompts into the keyword-enriched prompts favored by systems is essential for the user experience. Such a prompt refinement process is analogous to translating the prompt from "user languages" into "system languages". However, the scarcity of such parallel corpora makes it difficult to train a prompt refinement model. Inspired by zero-shot machine translation techniques, we introduce Prompt Refinement with Image Pivot (PRIP). PRIP innovatively uses the latent representation of a user-preferred image as an intermediary "pivot" between the user and system languages. It decomposes the refinement process into two data-rich tasks: inferring representations of user-preferred images from user languages and subsequently translating image representations into system languages. Thus, it can leverage abundant data for training. Extensive experiments show that PRIP substantially outperforms a wide range of baselines and effectively transfers to unseen systems in a zero-shot manner.<|reference_end|>
@article{zhan2024prompt,
    title={Prompt Refinement with Image Pivot for Text-to-Image Generation},
    author={Jingtao Zhan, Qingyao Ai, Yiqun Liu, Yingwei Pan, Ting Yao, Jiaxin
  Mao, Shaoping Ma, Tao Mei},
    journal={arXiv preprint arXiv:2407.00247},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00247},
    primaryClass={cs.CV}
}