统一压缩不同架构的特征，提升跨模型压缩效率。
Cross-architecture universal feature coding via distribution alignment
- 将CNN与Transformer特征统一为2D令牌格式。
- 通过截断与归一化对齐特征分布，提升压缩效果。
- 适合需跨架构压缩特征的工程场景。
特征编码在传输和存储语义表示而非原始像素的场景中日益重要。然而，现有方法多针对特定架构（如CNN或Transformer），难以应对实际中两种架构共存的情况。为此，本文提出跨架构通用特征编码（CAUFC）问题，旨在构建可统一压缩异构架构特征的编解码器。我们提出两步分布对齐方法：首先设计格式对齐方法，将CNN与Transformer特征统一为一致的2D令牌格式；其次提出特征值对齐方法，通过截断与归一化协调统计分布。作为首次探索CAUFC的工作，我们在图像分类任务上评估了该方法，实验表明其在率-精度权衡上优于架构专用基线。本工作标志着迈向异构模型架构通用特征压缩的重要一步。
原文摘要 · Abstract (English)
Feature coding has become increasingly important in scenarios where semantic representations rather than raw pixels are transmitted and stored. However, most existing methods are architecture-specific, targeting either CNNs or Transformers. This design limits their applicability in real-world scenarios where features from both architectures coexist. To address this gap, we introduce a new research problem: cross-architecture universal feature coding (CAUFC), which seeks to build a unified codec that can effectively compress features from heterogeneous architectures. To tackle this challenge, we propose a two-step distribution alignment method. First, we design the format alignment method that unifies CNN and Transformer features into a consistent 2D token format. Second, we propose the feature value alignment method that harmonizes statistical distributions via truncation and normalization. As a first attempt to study CAUFC, we evaluate our method on the image classification task. Experimental results demonstrate that our method achieves superior rate-accuracy trade-offs compared to the architecture-specific baseline. This work marks an initial step toward universal feature compression across heterogeneous model architectures.
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