Image-to-Image Translation: Methods and Applications

By Yingxue Pang, Jianxin Lin, Tao Qin, Zhibo Chen. IEEE transactions on multimedia, 2021

349 citations recorded by the source corpus.

Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations. I2I has drawn increasing attention and made tremendous progress in recent years because of its wide range of applications in many computer vision and image processing problems, such as image synthesis, segmentation, style transfer, restoration, and pose estimation. In this paper, we provide an overview of the I2I works developed in recent years. We will analyze the key techniques of the existing I2I works and clarify the main progress the community has made. Additionally, we will elaborate on the effect of I2I on the research and industry community and point out remaining challenges in related fields.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f13aadb4611d07a97832f636e67b42fdd7336d1d

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. Dong Chen
2. Fang Wen
3. Bo Zhang
4. Ting Zhang
5. Xiaoou Tang
6. Xiaogang Wang
7. Tengfei Wang
8. Jamal Bentahar

Citations in the corpus

• A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks (2023, background): “One example of this involves applying cartoon-style effects to images to change their appearance (Pang et al., 2022).”
• Pretraining is All You Need for Image-to-Image Translation (2022, background): “Plenty of works [34, 28] have been proposed to tackle the problem.”