  The problem of cross-modality person re-identification has been receiving
increasing attention recently, due to its practical significance. Motivated by
the fact that human usually attend to the difference when they compare two
similar objects, we propose a dual-path cross-modality feature learning
framework which preserves intrinsic spatial strictures and attends to the
difference of input cross-modality image pairs. Our framework is composed by
two main components: a Dual-path Spatial-structure-preserving Common Space
Network (DSCSN) and a Contrastive Correlation Network (CCN). The former embeds
cross-modality images into a common 3D tensor space without losing spatial
structures, while the latter extracts contrastive features by dynamically
comparing input image pairs. Note that the representations generated for the
input RGB and Infrared images are mutually dependant to each other. We conduct
extensive experiments on two public available RGB-IR ReID datasets, SYSU-MM01
and RegDB, and our proposed method outperforms state-of-the-art algorithms by a
large margin with both full and simplified evaluation modes.
