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 labeled samples, while an existing domain (i.e., the auxiliary
domain) is often available with a large number of labeled 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 furthermore challenging because the feature distributions of training samples from the web domain and consumer domain may differ tremendously in statistical properties. To effectively utilize training samples from both domains, domain adaptation techniques can be employed to learn robust classifiers that explicitly cope with the
considerable variation in feature distributions.

This special issue seeks high quality and original research on domain adaptation for vision applications. The goals of this special issue are three-fold: 1) investigating fundamental theories for domain adaptation, 2) presenting novel domain adaptation techniques applicable to at least one existing computer vision application, and 3) exploring new challenging vision applications for domain adaptation techniques.
Manuscripts are solicited to address a wide range of topics on domain adaptation techniques and applications with a focus on computer vision tasks, including but not limited to the following:

* Fundamental theory for domain adaptation
* Single source domain adaptation
* Multiple source domain adaptation
* Unsupervised domain adaptation
* Heterogeneous domain adaptation
* Online domain adaptation
* Cross-knowledge transfer
* Novel computer vision applications for domain adaptation
* Evaluation of domain adaptation algorithms and systems for specific vision applications