  Incorporating additional knowledge in the learning process can be beneficial
for several computer vision and machine learning tasks. Whether privileged
information originates from a source domain that is adapted to a target domain,
or as additional features available at training time only, using such
privileged (i.e., auxiliary) information is of high importance as it improves
the recognition performance and generalization. However, both primary and
privileged information are rarely derived from the same distribution, which
poses an additional challenge to the recognition task. To address these
challenges, we present a novel learning paradigm that leverages privileged
information in a domain adaptation setup to perform visual recognition tasks.
The proposed framework, named Adaptive SVM+, combines the advantages of both
the learning using privileged information (LUPI) paradigm and the domain
adaptation framework, which are naturally embedded in the objective function of
a regular SVM. We demonstrate the effectiveness of our approach on the publicly
available Animals with Attributes and INTERACT datasets and report
state-of-the-art results in both of them.
