Cross-architecture universal feature coding via distribution alignment

• URL: http://arxiv.org/abs/2506.12737v1
• Date: Sun, 15 Jun 2025 06:14:02 GMT
• Title: Cross-architecture universal feature coding via distribution alignment
• Authors: Changsheng Gao, Shan Liu, Feng Wu, Weisi Lin,
• Abstract summary: We introduce a new research problem: cross-architecture universal feature coding (CAUFC)<n>We propose a two-step distribution alignment method. First, we design the format alignment method that 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.<n>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.
• Score: 88.73189953617594
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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