Multi-granularity re-ranking for visible-infrared person re-identification

Visible-infrared person re-identification (VI-ReID) is a supplementary task of single-modality re-identification, which makes up for the defect of conventional re-identification under insufficient illumination. It is more challenging than single-modality ReID because, in addition to difficulties in pedestrian posture, camera shooting angle and background change, there are also difficulties in the cross-modality gap. Existing works only involve coarse-grained global features in the re-ranking calculation, which cannot effectively use fine-grained features. However, fine-grained features are particularly important due to the lack of information in cross-modality re-ID. To this end, the Q-center Multi-granularity K-reciprocal Re-ranking Algorithm (termed QCMR) is proposed, including a Q-nearest neighbour centre encoder (termed QNC) and a Multi-granularity K-reciprocal Encoder (termed MGK) for a more comprehensive feature representation. QNC converts the probe-corresponding modality features into gallery corresponding modality features through modality transfer to narrow the modality gap. MGK takes a coarse-grained mutual nearest neighbour as the dominant and combines a fine-grained nearest neighbour as a supplement for similarity measurement. Extensive experiments on two widely used VI-ReID benchmarks, SYSU-MM01 and RegDB have shown that our method achieves state-of-the-art results. Especially, the mAP of SYSU-MM01 is increased by 5.9% in all-search mode.