Feature coding has recently emerged as a key technique for efficient transmission of intermediate representations in distributed AI systems. Existing approaches largely follow a transform-based pipeline inherited from image and video coding, where the transform module is used to remove spatial structural redundancies in visual signals. However, our analysis indicates that such redundancies have already been largely removed during feature extraction, which reduces the necessity of the transform module. Building on this insight, we propose a new transform-free pipeline that directly encodes the extracted features via a vector quantization module and an entropy model. The proposed transform‑free framework jointly learns the quantization codebook and entropy model, enabling end‑to‑end optimization tailored to the inherent feature characteristics. Furthermore, the proposed method inherently avoids the computational complexity of the transform module. Experiments on features from diverse architectures and tasks demonstrate that our method achieves superior rate-distortion performance compared to transform-based baselines, while significantly reducing the encoding and decoding complexity.

Large models have achieved remarkable performance across various tasks, yet they incur significant computational costs and privacy concerns during both training and inference. Distributed deployment has emerged as a potential solution, but it necessitates the exchange of intermediate information between model segments, with feature representations serving as crucial information carriers. To optimize information exchange, feature coding is required to reduce transmission and storage overhead. Despite its importance, feature coding for large models remains an under-explored area. In this paper, we draw attention to large model feature coding and make three fundamental contributions. First, we introduce a comprehensive dataset encompassing diverse features generated by three representative types of large models. Second, we establish unified test conditions, enabling standardized evaluation pipelines and fair comparisons across future feature coding studies. Third, we introduce two baseline methods derived from widely used image coding techniques and benchmark their performance on the proposed dataset. These contributions aim to provide a foundation for future research and inspire broader engagement in this field. To support a long-term study, all source code and the dataset are made available at \href{https://github.com/chansongoal/LaMoFC}{https://github.com/chansongoal/LaMoFC}.

We propose an end-to-end learned scalable multilayer feature compression method. Our proposed method is illustrated in Figure 1 , where f 1 ,… ,f n stand for deep features at different layers. The deep feature f n denoting base layer is first transformed and quantized into the latent ${\hat y_n}$ . The latent ${\hat y_n}$ is then inversely transformed to reconstruct the feature as f ˆ n . In addition, the latent ${\hat y_n}$ is also fed into the entropy model of the previous-layer feature f n−1 as conditional information for the enhancement layer. The entropy model of the feature f n−1 takes both ${\hat y_{n - 1}}$ and ${\hat y_n}$ as inputs to improve compression efficiency.

In the field of Video Coding for Machines (VCM), scalable feature compression has attracted attention for its potential to support a variety of machine vision tasks. However, the existing scalable feature compression methods exhibit limited performance. To address this problem, we propose an end-to-end learned scalable multilayer feature compression method in this paper. First, we propose to leverage an end-to-end feature compression method, which can efficiently exploit redundancy among features through a learning approach, to improve compression efficiency. Second, we introduce a novel strategy involving the use of the transformed latent of the base layer as the conditional information for the enhancement layer. Given the learnable nature of our compression method, we propose to optimize the base layer and the enhancement layer jointly. The joint optimization encourages the base layer to produce more suitable conditional information for the enhancement layer. Comparative experiments against existing feature compression and image compression methods verify our approach’s remarkable performance improvements.

Feature compression is an important branch of video coding for machines (VCM). While existing methods draw inspiration from image compression, they have not fully utilized the unique characteristics of features. In this paper, we investigate feature characteristics in two key aspects: dimensionality and sparsity. Our analysis reveals that the low spatial dimensionality and high channel dimensionality of features make traditional 2D convolution-based methods, which usually downsample along spatial dimensions while increasing channels, unsuitable for feature compression. To address this, we propose compressing features using 3D convolution. Additionally, considering the sparsity characteristic, we propose applying sparse convolution to reduce model complexity. To thoroughly investigate the proposed 3D sparse convolution-based method, we verify it with various network structures and input features. Experimental results demonstrate the superiority of our proposed method over traditional 2D convolution-based approaches, highlighting its potential for effective feature compression.