  Visible-infrared person re-identification (VI-ReID) aims to match people with
the same identity between visible and infrared modalities. VI-ReID is a
challenging task due to the large differences in individual appearance under
different modalities. Existing methods generally try to bridge the cross-modal
differences at image or feature level, which lacks exploring the discriminative
embeddings. Effectively minimizing these cross-modal discrepancies relies on
obtaining representations that are guided by identity and consistent across
modalities, while also filtering out representations that are irrelevant to
identity. To address these challenges, we introduce a dynamic identity-guided
attention network (DIAN) to mine identity-guided and modality-consistent
embeddings, facilitating effective bridging the gap between different
modalities. Specifically, in DIAN, to pursue a semantically richer
representation, we first use orthogonal projection to fuse the features from
two connected coarse and fine layers. Furthermore, we first use dynamic
convolution kernels to mine identity-guided and modality-consistent
representations. More notably, a cross embedding balancing loss is introduced
to effectively bridge cross-modal discrepancies by above embeddings.
Experimental results on SYSU-MM01 and RegDB datasets show that DIAN achieves
state-of-the-art performance. Specifically, for indoor search on SYSU-MM01, our
method achieves 86.28% rank-1 accuracy and 87.41% mAP, respectively. Our code
will be available soon.
