  Visible-infrared person re-identification (VI-ReID) aims to retrieve images
of the same persons captured by visible (VIS) and infrared (IR) cameras.
Existing VI-ReID methods ignore high-order structure information of features
while being relatively difficult to learn a reasonable common feature space due
to the large modality discrepancy between VIS and IR images. To address the
above problems, we propose a novel high-order structure based middle-feature
learning network (HOS-Net) for effective VI-ReID. Specifically, we first
leverage a short- and long-range feature extraction (SLE) module to effectively
exploit both short-range and long-range features. Then, we propose a high-order
structure learning (HSL) module to successfully model the high-order
relationship across different local features of each person image based on a
whitened hypergraph network.This greatly alleviates model collapse and enhances
feature representations. Finally, we develop a common feature space learning
(CFL) module to learn a discriminative and reasonable common feature space
based on middle features generated by aligning features from different
modalities and ranges. In particular, a modality-range identity-center
contrastive (MRIC) loss is proposed to reduce the distances between the VIS,
IR, and middle features, smoothing the training process. Extensive experiments
on the SYSU-MM01, RegDB, and LLCM datasets show that our HOS-Net achieves
superior state-of-the-art performance. Our code is available at
\url{https://github.com/Jaulaucoeng/HOS-Net}.
