  A key topic in classification is the accuracy loss produced when the data
distribution in the training (source) domain differs from that in the testing
(target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and
object category recognition. In this paper, we present a novel domain
adaptation method that leverages multiple target domains (or sub-domains) in a
hierarchical adaptation tree. The core idea is to exploit the commonalities and
differences of the jointly considered target domains.
  Given the relevance of structural SVM (SSVM) classifiers, we apply our idea
to the adaptive SSVM (A-SSVM), which only requires the target domain samples
together with the existing source-domain classifier for performing the desired
adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
  As proof of concept we use HA-SSVM for pedestrian detection and object
category recognition. In the former we apply HA-SSVM to the deformable
part-based model (DPM) while in the latter HA-SSVM is applied to multi-category
classifiers. In both cases, we show how HA-SSVM is effective in increasing the
detection/recognition accuracy with respect to adaptation strategies that
ignore the structure of the target data. Since, the sub-domains of the target
data are not always known a priori, we shown how HA-SSVM can incorporate
sub-domain structure discovery for object category recognition.
