Cross-architecture universal feature coding via distribution alignment
Journal:
arXiv
Published Date:
Jun 15, 2025
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.
