  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.
