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
https://github.com/Jaulaucoeng/HOS-Net.

通过引入高阶结构信息的中级特征学习网络，有效解决了可见光 - 红外人物再识别中高阶结构信息的忽略、模态差异导致特征空间难学习的问题，并在多个数据集上取得了优于现有方法的性能。

基于高阶结构的中间特征学习用于可见 - 红外人员再识别

High-Order Structure Based Middle-Feature Learning for Visible-Infrared  Person Re-Identification

Heterogeneous graph neural networks (HGNNs) as an emerging technique have
shown superior capacity of dealing with heterogeneous information network
(HIN). However, most HGNNs follow a semi-supervised learning manner, which
notably limits their wide use in reality since labels are usually scarce in
real applications. Recently, contrastive learning, a self-supervised method,
becomes one of the most exciting learning paradigms and shows great potential
when there are no labels. In this paper, we study the problem of
self-supervised HGNNs and propose a novel co-contrastive learning mechanism for
HGNNs, named HeCo. Different from traditional contrastive learning which only
focuses on contrasting positive and negative samples, HeCo employs cross-view
contrastive mechanism. Specifically, two views of a HIN (network schema and
meta-path views) are proposed to learn node embeddings, so as to capture both
of local and high-order structures simultaneously. Then the cross-view
contrastive learning, as well as a view mask mechanism, is proposed, which is
able to extract the positive and negative embeddings from two views. This
enables the two views to collaboratively supervise each other and finally learn
high-level node embeddings. Moreover, to further boost the performance of HeCo,
two additional methods are designed to generate harder negative samples with
high quality. Besides the invariant factors, view-specific factors
complementally provide the diverse structure information between different
nodes, which also should be contained into the final embeddings. Therefore, we
need to further explore each view independently and propose a modified model,
called HeCo++. Specifically, HeCo++ conducts hierarchical contrastive learning,
including cross-view and intra-view contrasts, which aims to enhance the mining
of respective structures.

本文研究如何利用对比学习增强异构图神经网络的自我监督学习，提出了一种名为 HeCo 的新型协同对比学习机制，该机制使用交叉视图对比学习方法，学习节点嵌入以捕捉局部和高阶结构，并提出两种方法来生成更难的负样本以提高性能。最终获得包含不变因素和视图特定因素的高级节点嵌入。