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2019/20 I. félév
Árpád tér 2. II. em. 220. sz.
Gabriela Csurka (Naver Labs Europe, Meylan, France)
New Trends in Visual Domain Adaptation

I will give an overview of visual domain adaptation methods.
The first part will focus on the tendencies used by historical shallow
methods. Then I will discuss the effect of the success of deep
convolutional architectures on the field of domain adaption showing
that domain adaptation can benefit from these architectures in various
manner. Finally I will talk about how the advances in adversarial
learning and especially Generative Adversarial Networks yielded not
only to a new set of deep domain adaptation models for image
categorization, but also contributed to extend domain adaptation to
new tasks such as semantic segmentation. I will end with a few words
about domain adaptation for visual localization.

Biography: Gabriela Csurka is a principal scientist at Naver Labs
Europe. She obtained her Ph.D. degree (1996) in Computer Science
prepared, under the direction of Olivier Faugeras, on projective
representations of the three-dimensional environment from uncalibrated
stereo views. Main inventor of the bag-of-visual words (BOV) framework
which revolutionized the field of computer vision and became a de
facto standard for image classification for almost 10 years, she also
worked on digital watermarking for copyright protection, image
segmentation, cross-modal image retrieval and domain adaptation. She
is the editor of the book entitled "Domain Adaptation in Computer
Vision Applications" that appeared in 2017 in the Springer Series. Her
current research interests are in computer vision, including image
understanding, multi-view 3D reconstruction and visual localization.