Abstract
Feature compression, as an important branch of video coding for machines (VCM), has attracted significant attention and exploration. However, the existing methods mainly focus on intra-feature similarity, such as the Mean Squared Error (MSE) between the reconstructed and original features, while neglecting the importance of inter-feature relationships. In this paper, we analyze the inter-feature relationships, focusing on feature discriminability in machine vision and underscoring its significance in feature compression. To maintain the feature discriminability of reconstructed features, we introduce a discrimination metric for feature compression. The discrimination metric is designed to ensure that the distance between features of the same category is smaller than the distance between features of different categories. Furthermore, we explore the relationship between the discrimination metric and the discriminability of the original features. Experimental results confirm the effectiveness of the proposed discrimination metric and reveal there exists a trade-off between the discrimination metric and the discriminability of the original features.
Abstract (translated)
特征压缩作为机器视频编码（VCM）的一个重要分支，已经引起了广泛关注和探索。然而，现有的方法主要关注内特征相似性，如重构和原始特征之间的均方误差（MSE），而忽略了内特征之间的重要性关系。在本文中，我们分析了内特征之间的关系，重点关注机器视觉中的特征可识别性，并强调其在特征压缩中的重要性。为了保持重构特征的可识别性，我们引入了一个用于特征压缩的区分度度量。该区分度度量旨在确保同一类别特征之间的距离小于不同类别特征之间的距离。此外，我们还探讨了区分度度量与原始特征的可识别性之间的关系。实验结果证实了所提出的区分度度量的有效性，并揭示了区分度度量与原始特征可识别性之间存在一种权衡关系。
URL
https://arxiv.org/abs/2405.04044
