Naver Labs Europe, France
Recent Trends in Domain Adaptation for Visual Applications
The aim of this talk will be to give an overview of visual domain adaptation methods. First, I will briefly recall historical shallow methods. Then, I will discuss different manners to exploit deep convolutional architectures in visual domain adaptation, focusing on image categorization. In the last part of my talk, I will overview recent trends in domain adaptation, including deep discriminative models, adversarial and encoder-decoder based models, network parameter adaptation methods, semi-supervised and curriculum learning based models. I will present methods proposed in the literature for image classification, semantic segmentation, object detection and others.
Gabriela Csurka is a Principal Scientist at Naver Labs Europe, France. Her main research interests are in computer vision for image understanding, multi-view 3D reconstruction, visual localization, multi-modal information retrieval as well as domain adaptation and transfer learning. She contributed to around 100 scientific communications, several on the topic of DA, got the best paper award at the Transferring and Adapting Source Knowledge in Computer Vision Workshop (TaskCV) in 2016, participated with success in DA related challenges (ImageClefDA’14, VisDA’17), and gave invited talks on domain adaptation (ACIVS’15, Task-CV’17, OpenMIC’18 and Task-CV’19). In 2017 she edited a book on Domain Adaptation in Computer Vision Applications.