  RGB-infrared person re-identification is a challenging task due to the
intra-class variations and cross-modality discrepancy. Existing works mainly
focus on learning modality-shared global representations by aligning image
styles or feature distributions across modalities, while local feature from
body part and relationships between person images are largely neglected. In
this paper, we propose a Dual-level (i.e., local and global) Feature Fusion
(DF^2) module by learning attention for discriminative feature from local to
global manner. In particular, the attention for a local feature is determined
locally, i.e., applying a learned transformation function on itself. Meanwhile,
to further mining the relationships between global features from person images,
we propose an Affinities Modeling (AM) module to obtain the optimal intra- and
inter-modality image matching. Specifically, AM employes intra-class
compactness and inter-class separability in the sample similarities as
supervised information to model the affinities between intra- and
inter-modality samples. Experimental results show that our proposed method
outperforms state-of-the-arts by large margins on two widely used
cross-modality re-ID datasets SYSU-MM01 and RegDB, respectively.
