International Journal of Computer Vision Special Issue on Domain Adaptation for Vision Applications Call for Papers

Call for Papers International Journal of Computer Vision Special Issue on Domain Adaptation for Vision Applications 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 Guidelines for authors can be found at http://www.editorialmanager.com/visi/. Prospective authors should submit high quality, original manuscripts that have not appeared, nor are under consideration, in any other journal or conference. Papers submitted to this special issue should have a distinctive title using the format: SI-Domain Adaptation. All papers will be peer reviewed by experts in the field. Important Dates Manuscript submission: 1st March 2013 Preliminary results: 30th June 2013 Revisions due: 30th September 2013 Notification: 30thNovember 2013 Final manuscripts due: 30th December 2013 Anticipated publication: 1st or 2nd quarter 2014 Guest Editors Dr. Dong Xu Nanyang Technological University, Singapore dongxu@ntu.edu.sg Prof. Rama Chellappa University of Maryland, College Park, USA rama@umiacs.umd.edu Prof. Trevor Darrell University of California, Berkeley, USA trevor@eecs.berkeley.edu Dr. Hal Daumé III University of Maryland, College Park, USA <script>writeFooterInfo();</script>  <script type="text/javascript"> var infolink_pid = 176081; var infolink_wsid = 0; </script> <script type="text/javascript" src="http://resources.infolinks.com/js/infolinks_main.js"></script>  </BODY> </HTML>