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CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation

Generating photo-realistic images from a text description is a challenging problem in computer vision. Previ- ous works have shown promising performance to generate synthetic images conditional on text by Generative Adver- sarial Networks (GANs). In this paper, we focus on the category-consistent and relativistic diverse constraints to optimize the diversity of synthetic images.

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Cite this as

Tao Hu, Chengjiang Long, Chunxia Xiao (2024). Dataset: CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation. https://doi.org/10.57702/2w6l3p6j

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Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.48550/arXiv.2107.13516
Author	Tao Hu
More Authors	Chengjiang Long Chunxia Xiao
Homepage	https://arxiv.org/abs/2104.02474