# SegAttnGAN: Text to Image Generation with Segmentation Attention

arXiv: https://arxiv.org/abs/2005.12444 · HTML: https://mlsanity.com/paper/2005.12444/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 6 of 6)

- [Text-to-Image Synthesis: A Decade Survey](https://mlsanity.com/paper/2411.16164/) (2024)
- [DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generation](https://mlsanity.com/paper/2111.09267/) (2021)
- [Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation](https://mlsanity.com/paper/2209.14046/) (2022)
- [SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation](https://mlsanity.com/paper/2210.15235/) (2022)
- [TextCLIP: Text-Guided Face Image Generation And Manipulation Without Adversarial Training](https://mlsanity.com/paper/2309.11923/) (2023)
- [Face Mask Removal with Region-attentive Face Inpainting](https://mlsanity.com/paper/2409.06845/) (2024)

## Abstract (arXiv)

In this paper, we propose a novel generative network (SegAttnGAN) that utilizes additional segmentation information for the text-to-image synthesis task. As the segmentation data introduced to the model provides useful guidance on the generator training, the proposed model can generate images with better realism quality and higher quantitative measures compared with the previous state-of-art methods. We achieved Inception Score of 4.84 on the CUB dataset and 3.52 on the Oxford-102 dataset. Besides, we tested the self-attention SegAttnGAN which uses generated segmentation data instead of masks from datasets for attention and achieved similar high-quality results, suggesting that our model can be adapted for the text-to-image synthesis task.