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.

翻译：图像序数回归主要沿着利用类别顺序的研究路线进行。然而，序数回归中普遍存在的类别不平衡和类别重叠问题在很大程度上被忽视了。这导致少数类别的性能往往不尽如人意。本文提出一种基于可控图像生成的新框架CIG，直接解决这两个问题。我们的核心思想是在类别边界附近生成具有特定标签的额外训练样本，并且样本生成偏向于表示不足的类别。为实现可控图像生成，我们基于结构相似性、类别相似性和重建约束来分离图像的结构信息与类别信息。我们在三种不同的图像序数回归场景中评估了CIG新方法的有效性。结果表明，CIG能够灵活集成现有图像编码器或序数回归模型以提升性能，且对少数类别的提升更为显著。