Layout2image: Image Generation from Layout

By Bo Zhao, Weidong Yin, Lili Meng, Leonid Sigal. 2020

45 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Generative Models, Multimodal Learning

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. Dacheng Tao
2. Chaoyue Wang
3. Daqing Liu
4. Zuopeng Yang
5. Jie Yang
6. Tianfu Wu
7. Luc Van Gool
8. Song-Chun Zhu

In Generative Models

1. Dacheng Tao
2. Chaoyue Wang
3. Zuopeng Yang
4. Jie Yang
5. Daqing Liu
6. Tianfu Wu
7. Tobias Hinz
8. Robin Rombach

In Multimodal Learning

1. Chaoyue Wang
2. Dacheng Tao
3. Tianfu Wu
4. Daqing Liu

Citations in the corpus

• Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis (2020, methodology): “In contrast to the CAS, the classification accuracy metric used in the Layout2Im [20] is based on models trained with real image and tested on synthesized images, which may overlook the diversity of synthesized images.”
• Modeling Image Composition for Complex Scene Generation (2022, methodology): “Although CNN-based layout-to-image generation methods have achieved promising performance on texture synthesis, they may still suffer from accurately modeling the dependencies among pixels (or object parts), which hinders the model generated more realistic scene images.”