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Visual Domain Adaptation

Dept. of Computer Science (January 4, 2017)

SEMINAR SERIES : Distinguished Lecture

LENGTH : 81 min.
ACCESS : Open to all
SUMMARY : Domain adaptation (also called transfer learning) is an emerging research topic in computer vision. In some vision applications, the domain of interest (i.e., the target domain) contains very few or even no labelled samples, while an existing domain (i.e., the auxiliary domain) is often available with a large number of labelled examples. For example, millions of loosely labelled Flickr photos or YouTube videos can be readily obtained by using keywords based search. On the other hand, users may be interested in retrieving and organizing their own multimedia collections of images and videos at the semantic level, but may be reluctant to put forth the effort to annotate their photos and videos by themselves. This problem becomes furthermore challenging because the feature distributions of training samples from the web domain and consumer domain may differ tremendously in statistical properties. To explicitly cope with the feature distribution mismatch for the samples from different domains, in this talk I will describe our SVM based approaches for domain adaptation under different settings as well as their interesting applications in computer vision.  [Go to the full record in the library's catalogue]

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