Rifegan: Rich feature generation for text-to-image synthesis from prior knowledge

Jun Cheng, Fuxiang Wu*, Yanling Tian, Lei Wang, Dapeng Tao

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

85 Citations (Scopus)


Text-to-image synthesis is a challenging task that generates realistic images from a textual sequence, which usually contains limited information compared with the corresponding image and so is ambiguous and abstractive. The limited textual information only describes a scene partly, which will complicate the generation with complementing the other details implicitly and lead to low-quality images. To address this problem, we propose a novel rich feature generation text-to-image synthesis, called RiFeGAN, to enrich the given description. In order to provide additional visual details and avoid conflicting, RiFeGAN exploits an attention-based caption matching model to select and refine the compatible candidate captions from prior knowledge. Given enriched captions, RiFeGAN uses self-attentional embedding mixtures to extract features across them effectually and handle the diverging features further. Then it exploits multi-captions attentional generative adversarial networks to synthesize images from those features. The experiments conducted on widely-used datasets show that the models can generate images from enriched captions effectually and improve the results significantly.

Original languageEnglish
Article number9156682
Pages (from-to)10908-10917
Number of pages10
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publication statusPublished - 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
Duration: 2020 Jun 142020 Jun 19

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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