Guest Editor's Introduction to the Special Issue on Domain Adaptation for Vision Applications

摘要

Domain adaptation 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/ source domain) is often available with a large number of labelled examples. For example, millions of loosely labeled Flickr photos or YouTube videos can be readily obtained by using keywords (also called tags) 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 more challenging because the feature distributions of training samples from the web domain and consumer domain may significantly differ in statistical properties. In order to effectively utilize the training samples from different domains, domain adaptation techniques aim to explicitly cope with variations in feature distributions.