Title: DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformation Show Chinese title

Title: DT-UFC：通过峰值到平衡分布变换的通用大模型特征编码

Abstract: Like image coding in visual data transmission, feature coding is essential for the distributed deployment of large models by significantly reducing transmission and storage overhead. However, prior studies have mostly targeted task- or model-specific scenarios, leaving the challenge of universal feature coding across diverse large models largely unaddressed. In this paper, we present the first systematic study on universal feature coding for large models. The key challenge lies in the inherently diverse and distributionally incompatible nature of features extracted from different models. For example, features from DINOv2 exhibit highly peaky, concentrated distributions, while those from Stable Diffusion 3 (SD3) are more dispersed and uniform. This distributional heterogeneity severely hampers both compression efficiency and cross-model generalization. To address this, we propose a learned peaky-to-balanced distribution transformation, which reshapes highly skewed feature distributions into a common, balanced target space. This transformation is non-uniform, data-driven, and plug-and-play, enabling effective alignment of heterogeneous distributions without modifying downstream codecs. With this alignment, a universal codec trained on the balanced target distribution can effectively generalize to features from different models and tasks. We validate our approach on three representative large models-LLaMA3, DINOv2, and SD3-across multiple tasks and modalities. Extensive experiments show that our method achieves notable improvements in both compression efficiency and cross-model generalization over task-specific baselines. All source code will be released for future research. Show Chinese abstract

Abstract: 如同视觉数据传输中的图像编码一样，特征编码对于通过显著减少传输和存储开销来实现大型模型的分布式部署至关重要。然而，先前的研究大多针对特定任务或特定模型的情景，对跨多种大型模型的通用特征编码挑战却鲜有涉及。本文首次系统性地研究了大型模型的通用特征编码问题。关键挑战在于从不同模型中提取的特征具有本质上多样化且分布上不兼容的特性。例如，DINOv2 提取的特征表现出高度尖峰、集中的分布，而来自 Stable Diffusion 3（SD3）的特征则更加分散且均匀。这种分布上的异质性严重阻碍了压缩效率和跨模型的泛化能力。为了解决这个问题，我们提出了一种学习型的尖峰到平衡分布转换方法，该方法将高度偏斜的特征分布重新塑造为一个共同的、平衡的目标空间。这种转换是非均匀的、数据驱动的，并且可以即插即用，能够在不对下游编解码器进行修改的情况下有效地对齐异构分布。通过这种对齐，一个在平衡目标分布上训练的通用编解码器能够有效泛化到来自不同模型和任务的特征。我们在三个代表性大型模型——LLaMA3、DINOv2 和 SD3——以及多个任务和模态上验证了我们的方法。广泛的实验表明，与任务特定的基线相比，我们的方法在压缩效率和跨模型泛化方面取得了显著改进。所有源代码将被公开以供未来研究使用。