Before revision:  Existing methods mainly focus on global-level modality alignment, whereas neglect sample-level modality divergence to some extent , leading to performance degradation. 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)
After revision:  Existing methods mainly focus on global-level modality alignment, whereas neglect sample-level modality divergence to some extent , leading to meaningless region representations. In this paper, we propose a graph-enabled distribution matching solution, dubbed Geometry-Guided Dual-Alignment learning framework (G^2DA)