  Image ordinal regression has been mainly studied along the line of exploiting
the order of categories. However, the issues of class imbalance and category
overlap that are very common in ordinal regression were largely overlooked. As
a result, the performance on minority categories is often unsatisfactory. In
this paper, we propose a novel framework called CIG based on controllable image
generation to directly tackle these two issues. Our main idea is to generate
extra training samples with specific labels near category boundaries, and the
sample generation is biased toward the less-represented categories. To achieve
controllable image generation, we seek to separate structural and categorical
information of images based on structural similarity, categorical similarity,
and reconstruction constraints. We evaluate the effectiveness of our new CIG
approach in three different image ordinal regression scenarios. The results
demonstrate that CIG can be flexibly integrated with off-the-shelf image
encoders or ordinal regression models to achieve improvement, and further, the
improvement is more significant for minority categories.
