  This paper pays close attention to the cross-modality visible-infrared person
re-identification (VI Re-ID) task, which aims to match pedestrian samples
between visible and infrared modes. In order to reduce the modality-discrepancy
between samples from different cameras, most existing works usually use
constraints based on Euclidean metric. Because of the Euclidean based distance
metric strategy cannot effectively measure the internal angles between the
embedded vectors, the existing solutions cannot learn the angularly
discriminative feature embedding. Since the most important factor affecting the
classification task based on embedding vector is whether there is an angularly
discriminative feature space, in this paper, we present a new loss function
called Enumerate Angular Triplet (EAT) loss. Also, motivated by the knowledge
distillation, to narrow down the features between different modalities before
feature embedding, we further present a novel Cross-Modality Knowledge
Distillation (CMKD) loss. Benefit from the above two considerations, the
embedded features are discriminative enough in a way to tackle
modality-discrepancy problem. The experimental results on RegDB and SYSU-MM01
datasets have demonstrated that the proposed method is superior to the other
most advanced methods in terms of impressive performance. Code is available at
https://github.com/IVIPLab/LCCRF.
