  RGB-Infrared (IR) cross-modality person re-identification (re-ID), which aims
to search an IR image in RGB gallery or vice versa, is a challenging task due
to the large discrepancy between IR and RGB modalities. Existing methods
address this challenge typically by aligning feature distributions or image
styles across modalities, whereas the very useful similarities among gallery
samples of the same modality (i.e. intra-modality sample similarities) is
largely neglected. This paper presents a novel similarity inference metric
(SIM) that exploits the intra-modality sample similarities to circumvent the
cross-modality discrepancy targeting optimal cross-modality image matching. SIM
works by successive similarity graph reasoning and mutual nearest-neighbor
reasoning that mine cross-modality sample similarities by leveraging
intra-modality sample similarities from two different perspectives. Extensive
experiments over two cross-modality re-ID datasets (SYSU-MM01 and RegDB) show
that SIM achieves significant accuracy improvement but with little extra
training as compared with the state-of-the-art.
