CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis

By Jiadong Liang, Wenjie Pei, Feng Lu. 2020

72 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Generative Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Trevor Darrell
2. Anna Rohrbach
3. Changsheng Xu
4. Dacheng Tao
5. Luc Van Gool
6. Ming-Hsuan Yang
7. Xiaogang Wang
8. Li Fei-Fei

In Generative Models

1. Fei Wu
2. Xiulong Yang
3. Shihao Ji
4. Martin Takác
5. Changsheng Xu
6. Hao Tang
7. Xiaoyuan Jing
8. Trevor Darrell

Citations in the corpus

• Improving Text-to-Image Synthesis Using Contrastive Learning (2021, methodology): “Recently, a great number of studies [1, 4, 6, 12, 13, 14, 17, 18, 19, 22, 23, 24, 25, 32, 33, 36, 42, 43, 44, 45, 46, 47, 48] present promising results on the text-to-image synthesis task, most of which make use of GANs as the backbone model.”
• DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis (2020, methodology): “We also compared with more recent models [22,26,39,55].”
• Benchmark for Compositional Text-to-Image Synthesis (2021, background): “Other approaches explored introducing memory mechanisms [63, 24], cycle-consistency constraint [37, 17], siamese networks [57, 47], and prior knowledge [36, 4] to improve the text-to-image synthesis performance.”