  RGB-Infrared (IR) person re-identification aims to retrieve
person-of-interest from heterogeneous cameras, easily suffering from large
image modality discrepancy caused by different sensing wavelength ranges.
Existing work usually minimizes such discrepancy by aligning domain
distribution of global features, while neglecting the intra-modality structural
relations between semantic parts. This could result in the network overly
focusing on local cues, without considering long-range body part dependencies,
leading to meaningless region representations. In this paper, we propose a
graph-enabled distribution matching solution, dubbed Geometry-Guided
Dual-Alignment (G2DA) learning, for RGB-IR ReID. It can jointly encourage the
cross-modal consistency between part semantics and structural relations for
fine-grained modality alignment by solving a graph matching task within a
multi-scale skeleton graph that embeds human topology information.
Specifically, we propose to build a semantic-aligned complete graph into which
all cross-modality images can be mapped via a pose-adaptive graph construction
mechanism. This graph represents extracted whole-part features by nodes and
expresses the node-wise similarities with associated edges. To achieve the
graph-based dual-alignment learning, an Optimal Transport (OT) based structured
metric is further introduced to simultaneously measure point-wise relations and
group-wise structural similarities across modalities. By minimizing the cost of
an inter-modality transport plan, G2DA can learn a consistent and
discriminative feature subspace for cross-modality image retrieval.
Furthermore, we advance a Message Fusion Attention (MFA) mechanism to
adaptively reweight the information flow of semantic propagation, effectively
strengthening the discriminability of extracted semantic features.
