Abstract
The split-inference paradigm divides an artificial intelligence (AI) model into two parts. This necessitates the transfer of intermediate feature data between the two halves. Here, effective compression of the feature data becomes vital. In this paper, we employ Z-score normalization to efficiently recover the compressed feature data at the decoder side. To examine the efficacy of our method, the proposed method is integrated into the latest Feature Coding for Machines (FCM) codec standard under development by the Moving Picture Experts Group (MPEG). Our method supersedes the existing scaling method used by the current standard under development. It both reduces the overhead bits and improves the end-task accuracy. To further reduce the overhead in certain circumstances, we also propose a simplified method. Experiments show that using our proposed method shows 17.09% reduction in bitrate on average across different tasks and up to 65.69% for object tracking without sacrificing the task accuracy.
Abstract (translated)
分而推理的范式将人工智能（AI）模型分为两个部分。这需要在两半之间传输中间特征数据。在此过程中，有效压缩这些特征数据变得至关重要。本文中，我们采用Z分数标准化方法，在解码器端高效地恢复压缩后的特征数据。为了检验该方法的有效性，我们将提出的方法集成到了由国际电信联盟视频编码专家组（MPEG）开发的最新《机器特征编码》（Feature Coding for Machines, FCM）编解码标准中。我们的方法超越了当前正在开发的标准使用的现有缩放方法，在减少开销比特的同时提高了终端任务准确性。为了进一步在某些情况下降低开销，我们还提出了一种简化的方法。实验表明，使用我们提出的方法在不同任务中的平均比特率减少了17.09%，对于物体跟踪任务甚至最多可减少65.69%，而不会牺牲任务的精度。
URL
https://arxiv.org/abs/2512.09235
