Prompt Refinement with Image Pivot for Text-to-Image Generation

• URL: http://arxiv.org/abs/2407.00247v1
• Date: Fri, 28 Jun 2024 22:19:24 GMT
• Title: Prompt Refinement with Image Pivot for Text-to-Image Generation
• Authors: Jingtao Zhan, Qingyao Ai, Yiqun Liu, Yingwei Pan, Ting Yao, Jiaxin Mao, Shaoping Ma, Tao Mei,
• Abstract summary: We introduce Prompt Refinement with Image Pivot (PRIP) for text-to-image generation. PRIP decomposes refinement process into two data-rich tasks: inferring representations of user-preferred images from user languages and translating image representations into system languages. It substantially outperforms a wide range of baselines and effectively transfers to unseen systems in a zero-shot manner.
• Score: 103.63292948223592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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