  Visible-infrared person re-identification (VI Re-ID) aims to match person
images between the visible and infrared modalities. Existing VI Re-ID methods
mainly focus on extracting homogeneous structural relationships in an image,
i.e. the relations between local features, while ignoring the heterogeneous
correlation of local features in different modalities. The heterogeneous
structured relationship is crucial to learn effective identity representations
and perform cross-modality matching. In this paper, we model the homogenous
structural relationship by a modality-specific graph within individual modality
and then mine the heterogeneous structural correlation with the
modality-specific graph of visible and infrared modality. First, the
homogeneous structured graph (HOSG) mines one-vs.-rest relation between an
arbitrary node (local feature) and all the rest nodes within a visible or
infrared image to learn effective identity representation. Second, to find
cross-modality identity-consistent correspondence, the heterogeneous graph
alignment module (HGAM) further measures the relational edge strength between
local node features of two modalities with routing search way. Third, we
propose the cross-modality cross-correlation (CMCC) loss to extract the
modality invariance of feature representations of visible and infrared graphs.
CMCC computes the mutual information between modalities and expels semantic
redundancy. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate
that our method outperforms state-of-the-arts with a gain of 13.73\% and 9.45\%
Rank1/mAP. The code is available at
https://github.com/fegnyujian/Homogeneous-and-Heterogeneous-Relational-Graph.
