Visible-infrared person re-identification (VI-reID) aims at matching
cross-modality pedestrian images captured by disjoint visible or infrared
cameras. Existing methods alleviate the cross-modality discrepancies via
designing different kinds of network architectures. Different from available
methods, in this paper, we propose a novel parameter optimizing paradigm,
parameter hierarchical optimization (PHO) method, for the task of VI-ReID. It
allows part of parameters to be directly optimized without any training, which
narrows the search space of parameters and makes the whole network more easier
to be trained. Specifically, we first divide the parameters into different
types, and then introduce a self-adaptive alignment strategy (SAS) to
automatically align the visible and infrared images through transformation.
Considering that features in different dimension have varying importance, we
develop an auto-weighted alignment learning (AAL) module that can automatically
weight features according to their importance. Importantly, in the alignment
process of SAS and AAL, all the parameters are immediately optimized with
optimization principles rather than training the whole network, which yields a
better parameter training manner. Furthermore, we establish the cross-modality
consistent learning (CCL) loss to extract discriminative person representations
with translation consistency. We provide both theoretical justification and
empirical evidence that our proposed PHO method outperform existing VI-reID
approaches.

可见 - 红外行人再识别（VI-reID）通过设计不同类型的网络架构来减小跨模态差异，本文提出了一种新颖的参数优化方法 —— 参数分层优化（PHO）方法，它允许直接优化部分参数而无需任何训练，缩小了参数搜索空间且使整个网络更易训练，此外，本文还引入了自适应对齐策略（SAS）来通过变换自动对齐可见光和红外图像，开发了自动加权对齐学习（AAL）模块来根据特征的重要性自动加权，同时，通过优化原则而非整个网络的训练，实现了 SAS 和 AAL 的对齐过程中的所有参数的即时优化，生成更好的参数训练方式，并且引入了跨模态一致性学习（CCL）损失来提取具有平移一致性的判别性人物表示，理论和实证结果都表明我们提出的 PHO 方法优于现有的 VI-reID 方法。