LatteGAN: Visually Guided Language Attention for Multi-Turn Text-Conditioned Image Manipulation

Shoya Matsumori, Yuki Abe, Kosuke Shingyouchi, Komei Sugiura, Michita Imai

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Text-guided image manipulation tasks have recently gained attention in the vision-and-language community. While most of the prior studies focused on single-turn manipulation, our goal in this paper is to address the more challenging multi-turn image manipulation (MTIM) task. Previous models for this task successfully generate images iteratively, given a sequence of instructions and a previously generated image. However, this approach suffers from under-generation and a lack of generated quality of the objects that are described in the instructions, which consequently degrades the overall performance. To overcome these problems, we present a novel architecture called a Visually Guided Language Attention GAN (LatteGAN). Here, we address the limitations of the previous approaches by introducing a Visually Guided Language Attention (Latte) module, which extracts fine-grained text representations for the generator, and a Text-Conditioned U-Net discriminator architecture, which discriminates both the global and local representations of fake or real images. Extensive experiments on two distinct MTIM datasets, CoDraw and i-CLEVR, demonstrate the state-of-the-art performance of the proposed model. The code is available online (

Original languageEnglish
Pages (from-to)160521-160532
Number of pages12
JournalIEEE Access
Publication statusPublished - 2021


  • Generative adversarial network (GAN)
  • multi-turn text-conditioned image manipulation

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering


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