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Open Access

Feature-Grounded Single-Stage Text-to-Image Generation

School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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Abstract

Recently, Generative Adversarial Networks (GANs) have become the mainstream text-to-image (T2I) framework. However, a standard normal distribution noise of inputs cannot provide sufficient information to synthesize an image that approaches the ground-truth image distribution. Moreover, the multistage generation strategy results in complex T2I applications. Therefore, this study proposes a novel feature-grounded single-stage T2I model, which considers the "real" distribution learned from training images as one input and introduces a worst-case-optimized similarity measure into the loss function to enhance the model’s generation capacity. Experimental results on two benchmark datasets demonstrate the competitive performance of the proposed model in terms of the Frechet inception distance and inception score compared to those of some classical and state-of-the-art models, showing the improved similarities among the generated image, text, and ground truth.

Keywords

Generative Adversarial Network (GAN)text-to-image (T2I)feature-groundedsingle-stage generation

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Tsinghua Science and Technology
Volume 29 Issue 2,
April 2024
Pages 469-480
DOI: 10.26599/TST.2023.9010023
Cite this article:
Zhou Y, Wang P, Xiang L, et al. Feature-Grounded Single-Stage Text-to-Image Generation. Tsinghua Science and Technology, 2024, 29(2): 469-480. https://doi.org/10.26599/TST.2023.9010023

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Received: 16 November 2022
Revised: 22 March 2023
Accepted: 26 March 2023
Published: 22 September 2023
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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