DMOFC: Discrimination Metric-Optimized Feature Compression
This work addresses feature compression for video coding for machines, focusing on improving discriminability, but it appears incremental as it builds on existing methods by adding a metric.
The paper tackled the problem of feature compression for machine vision by introducing a discrimination metric to maintain inter-feature relationships, resulting in experimental confirmation of its effectiveness and a revealed trade-off with original feature discriminability.
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.