发表机构
Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对序数回归中因主观标注产生的问题，提出D3O动态分布蒸馏框架，通过自蒸馏用序数标签分布的训练驱动演化取代静态监督，引入对比序数感知标签增强模块和基于CDF的跨层交互蒸馏机制，实验证明其优于现有方法。
AI 中文摘要
序数回归广泛应用于标签离散但本质上有序的场景。然而在实践中，序数标签常通过主观人为判断对潜在连续语义进行离散化获得，导致边界模糊和标注噪声。这种不确定性挑战了依赖固定监督目标的现有方法。为此提出D3O，一种动态分布蒸馏框架，通过自蒸馏用序数标签分布的训练驱动演化取代静态监督。引入对比序数感知标签增强模块利用视觉语言对齐恢复捕捉类间模糊性和实例级不确定性的精细标签分布，设计基于CDF的跨层交互蒸馏机制在网络层次结构中传播累积序数结构。在四个一般序数回归任务上的大量实验表明D3O始终优于现有方法，突出了动态监督在学习超越固定目标的鲁棒序数表示方面的有效性，代码将公开可用。
英文摘要
Ordinal regression is widely used in scenarios where labels are discrete yet inherently ordered. In practice, however, ordinal labels are often obtained by discretizing underlying continuous semantics through subjective human judgment, resulting in ambiguous boundaries and annotation noise. Such uncertainty challenges existing methods that rely on fixed supervision targets, which may reinforce biased ordering under subjective annotations. To address this limitation, we propose D3O, a dynamic distribution distillation framework that replaces static supervision with training-driven evolution of ordinal label distributions via self-distillation. Specifically, we introduce a contrastive ordinal-aware label enhancement module that leverages vision-language alignment to recover refined label distributions capturing both inter-class ambiguity and instance-level uncertainty. Furthermore, we design a CDF-based cross-layer interaction distillation mechanism to propagate cumulative ordinal structure across network hierarchy, ensuring consistent ordinal geometry in intermediate representations. Extensive experiments on four general ordinal regression tasks demonstrate that our proposed D3O consistently outperforms existing approaches, particularly under severe class imbalance and noisy supervision. These results highlight the effectiveness of dynamic supervision in learning robust ordinal representations beyond fixed targets. The code will be publicly available.
Comments10 pages, 5 figures, ACMMM2026
