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 burden. However, prior studies have mostly targeted task- or model-specific scenarios, leaving the challenge of universal feature coding across diverse large models largely unexplored. 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 has been made available at https://github.com/chansongoal/DT-UFC. Show Chinese abstract

Abstract: 像视觉数据传输中的图像编码一样，特征编码对于大型模型的分布式部署至关重要，因为它可以显著减少传输和存储负担。 然而，先前的研究主要针对特定任务或模型的场景，使得在不同大型模型之间进行通用特征编码的挑战尚未得到充分探索。 在本文中，我们提出了对大型模型通用特征编码的第一个系统性研究。 关键挑战在于从不同模型中提取的特征本质上具有多样性和分布不兼容性。 例如，DINOv2的特征表现出高度尖峰和集中分布，而Stable Diffusion 3（SD3）的特征则更加分散和均匀。 这种分布异质性严重阻碍了压缩效率和跨模型泛化能力。 为了解决这个问题，我们提出了一种学习的尖峰到平衡分布转换，将高度偏斜的特征分布重塑为一个共同的平衡目标空间。 这种转换是非均匀的、数据驱动的，并且可以即插即用，能够在不修改下游编解码器的情况下有效对齐异构分布。 通过这种对齐，可以在平衡目标分布上训练的通用编解码器能够有效地推广到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）上进行了多个任务和模态的验证。 大量实验表明，与特定任务基线相比，我们的方法在压缩效率和跨模型泛化方面都取得了显著改进。 所有源代码已发布在https://github.com/chansongoal/DT-UFC。