Before revision:  This paperattempts to find RGB-IR ReID solutions from tackling sample-level modality difference, and presents a Geometry-Guided Dual-Alignment learning framework (G^2DA) , which jointly enhances modality-invariance and reinforces discriminability with human topological structure in features to boost the overall matching performance . Specifically, G^2DA extracts accurate body part features with a pose estimator, serving as a semantic bridge complementing the missing local details in global descriptor.
After revision:  This paperattempts to find RGB-IR ReID solutions from tackling sample-level modality difference, and presents a 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, G^2DA extracts accurate body part features with a pose estimator, serving as a semantic bridge complementing the missing local details in global descriptor.