Virtual and Real World Adaptationfor Pedestrian Detection
By David Vázquez, A. L. Peña, Javier Marín, Daniel Ponsa, David Gerónimo Gómez. 2014
10 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kate Saenko
- Trevor Darrell
- Mingsheng Long
- Jianmin Wang
- Judy Hoffman
- Yoshua Bengio
- Sergey Levine
- Jiashi Feng
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Kate Saenko
- Ming-Hsuan Yang
- Dacheng Tao
- Yi Yang
- Xiaogang Wang
- Andrew Zisserman
Citations in the corpus
- Domain-Adversarial Training of Neural Networks (2015, background): “…example is training an image classifier on synthetic or semi-synthetic images, which may come in abundance and be fully labeled, but which inevitably have a distribution that is different from real images (Liebelt and Schmid, 2010; Stark et al., 2010; V´azquez et al., 2014; Sun and Saenko, 2014).”
- From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains (2014, background): “Unsupervised adaptation of detectors was explored in [19] for the pedestrian class.”