Norm-Aware Margin Assignment for Person Re-Identification

Abstract

Margin-based metric losses have shown great success in Person Re-identification and Face Verification. But most existing works adopt a fixed class-level margin regardless of the difference between each training sample. This paper proposes a Norm-Aware Margin Assignment (NAMA) scheme to dynamically adjust the weight of each sample during training. Combined with the existing margin-based classification losses, NAMA improves the robustness of feature embedding by assigning larger margins to more recognizable samples. NAMA is a fully trainable module that automatically models the correlation between the optimal margin and image quality during back-propagation without supervision. To stabilize the training and make the assigned margin more controllable, we introduce a margin re-balance mechanism to align the expectation of learned margins to a pre-defined value. Extensive experiments on three popular ReID benchmarks validate the effectiveness of our NAMA method. Code will be publicly available at: https://github.com/huangzongheng/NAMA.