Deep Imbalanced Regression via Hierarchical Classification Adjustment
Haipeng Xiong Angela Yao
Regression tasks in computer vision, such as age estimation or counting, are
often formulated into classification by quantizing the target space into
classes. Yet real-world data is often imbalanced -- the majority of training
samples lie in a head range of target values, while a minority of samples span
a usually larger tail range. By selecting the class quantization, one can
adjust imbalanced regression targets into balanced classification outputs,
though there are trade-offs in balancing classification accuracy and
quantization error. To improve regression performance over the entire range of
data, we propose to construct hierarchical classifiers for solving imbalanced
regression tasks. The fine-grained classifiers limit the quantization error
while being modulated by the coarse predictions to ensure high accuracy.
Standard hierarchical classification approaches, however, when applied to the
regression problem, fail to ensure that predicted ranges remain consistent
across the hierarchy. As such, we propose a range-preserving distillation
process that can effectively learn a single classifier from the set of
hierarchical classifiers. Our novel hierarchical classification adjustment
(HCA) for imbalanced regression shows superior results on three diverse tasks:
age estimation, crowd counting and depth estimation. We will release the source
code upon acceptance.