【文件属性】：
文件名称：Domain Adaptation for Visual Recognition
文件大小：2.74MB
文件格式：PDF
更新时间：2022-01-27 13:20:31
目标识别
Domain adaptation is an active, emerging research area that attempts
to address the changes in data distribution across training and testing
datasets. With the availability of a multitude of image acquisition sen-
sors, variations due to illumination, and viewpoint among others, com-
puter vision applications present a very natural test bed for evaluating
domain adaptation methods. In this monograph, we provide a compre-
hensive overview of domain adaptation solutions for visual recognition
problems. By starting with the problem description and illustrations,
we discuss three adaptation scenarios namely, (i) unsupervised adap-
tation where the “source domain” training data is partially labeled
and the “target domain” test data is unlabeled, (ii) semi-supervised
adaptation where the target domain also has partial labels, and (iii)
multi-domain heterogeneous adaptation which studies the previous two
settings with the source and/or target having more than one domain,
and accounts for cases where the features used to represent the data
in each domain are different. For all these topics we discuss existing
adaptation techniques in the literature, which are motivated by the
principles of max-margin discriminative learning, manifold learning,
sparse coding, as well as low-rank representations. These techniques
have shown improved performance on a variety of applications such
as object recognition, face recognition, activity analysis, concept clas-
sification, and person detection. We then conclude by analyzing the
challenges posed by the realm of “big visual data”, in terms of the
generalization ability of adaptation algorithms to unconstrained data
acquisition as well as issues related to their computational tractability,
and draw parallels with the efforts from vision community on image
transformation models, and invariant descriptors so as to facilitate im-
proved understanding of vision problems under uncertainty.