  This paper pays close attention to the cross-modality visible-infrared person
re-identification (VI Re-ID) task, which aims to match human samples between
visible and infrared modes. In order to reduce the discrepancy between features
of different modalities, most existing works usually use constraints based on
Euclidean metric. Since the Euclidean based distance metric cannot effectively
measure the internal angles between the embedded vectors, the above methods
cannot learn the angularly discriminative feature embedding. Because the most
important factor affecting the classification task based on embedding vector is
whether there is an angularly discriminativ feature space, in this paper, we
propose 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 new
Cross-Modality Knowledge Distillation (CMKD) loss. The experimental results on
RegDB and SYSU-MM01 datasets have shown that the proposed method is superior to
the other most advanced methods in terms of impressive performance.
