  Unsupervised domain adaptation (UDA) in image classification remains a big
challenge. In existing UDA image dataset, classes are usually organized in a
flattened way, where a plain classifier can be trained. Yet in some scenarios,
the flat categories originate from some base classes. For example, buggies
belong to the class bird. We define the classification task where classes have
characteristics above and the flat classes and the base classes are organized
hierarchically as hierarchical image classification. Intuitively, leveraging
such hierarchical structure will benefit hierarchical image classification,
e.g., two easily confusing classes may belong to entirely different base
classes. In this paper, we improve the performance of classification by fusing
features learned from a hierarchy of labels. Specifically, we train feature
extractors supervised by hierarchical labels and with UDA technology, which
will output multiple features for an input image. The features are subsequently
concatenated to predict the finest-grained class. This study is conducted with
a new dataset named Lego-15. Consisting of synthetic images and real images of
the Lego bricks, the Lego-15 dataset contains 15 classes of bricks. Each class
originates from a coarse-level label and a middle-level label. For example,
class "85080" is associated with bricks (coarse) and bricks round (middle). In
this dataset, we demonstrate that our method brings about consistent
improvement over the baseline in UDA in hierarchical image classification.
Extensive ablation and variant studies provide insights into the new dataset
and the investigated algorithm.
