  Hierarchical classification aims to sort the object into a hierarchical
structure of categories. For example, a bird can be categorized according to a
three-level hierarchy of order, family, and species. Existing methods commonly
address hierarchical classification by decoupling it into a series of
multi-class classification tasks. However, such a multi-task learning strategy
fails to fully exploit the correlation among various categories across
different levels of the hierarchy. In this paper, we propose Label Hierarchy
Transition (LHT), a unified probabilistic framework based on deep learning, to
address the challenges of hierarchical classification. The LHT framework
consists of a transition network and a confusion loss. The transition network
focuses on explicitly learning the label hierarchy transition matrices, which
has the potential to effectively encode the underlying correlations embedded
within class hierarchies. The confusion loss encourages the classification
network to learn correlations across different label hierarchies during
training. The proposed framework can be readily adapted to any existing deep
network with only minor modifications. We experiment with a series of public
benchmark datasets for hierarchical classification problems, and the results
demonstrate the superiority of our approach beyond current state-of-the-art
methods. Furthermore, we extend our proposed LHT framework to the skin lesion
diagnosis task and validate its great potential in computer-aided diagnosis.
The code of our method is available at
\href{https://github.com/renzhenwang/label-hierarchy-transition}{https://github.com/renzhenwang/label-hierarchy-transition}.
