  Unsupervised domain adaptation aims to train a classification model from the
labeled source domain for the unlabeled target domain. Since the data
distributions of the two domains are different, the model often performs poorly
on the target domain. Existing methods align the feature distributions of the
source and target domains and learn domain-invariant features to improve the
performance of the model. However, the features are usually aligned as a whole,
and the domain adaptation task fails to serve the classification, which will
ignore the class information and lead to misalignment.In this paper, we
investigate those features that should be used for domain alignment, introduce
prior knowledge to extract foreground features to guide the domain adaptation
task for classification tasks, and perform alignment in the local structure of
objects. We propose a method called Foreground Object Structure Transfer(FOST).
The key to FOST is the new clustering based condition, which combines the
relative position relationship of foreground objects. Based on this conditions,
FOST makes the data distribution of the same class more compact in geometry. In
practice, since the label of the target domain is not available, we use the
clustering information of the source domain to assign pseudo labels to the
target domain samples, and then according to the source domain data prior
knowledge guides those positive features to maximum the inter-class distance
between different classes and mimimum the intra-class distance. Extensive
experimental results on various benchmarks ($i.e.$ ImageCLEF-DA, Office-31,
Office-Home, Visda-2017) under different domain adaptation settings prove that
our FOST compares favorably against the existing state-of-the-art domain
adaptation methods.
