RMHNet: A Relation-Aware Multi-granularity Hierarchical Network for Person Re-identification.

Person re-identification (re-id) expects to find a picture according to a few clues that refer to the same pedestrian from an image database caught from different locations. Previous studies show that the exploration of local features may help for capturing more robust feature representations. However, part-level characteristics are demonstrated useful but easily confused with comparable attributes in corresponding locations. For this problem, we propose an effective relation-aware multi-granularity hierarchical network (RMHNet) to explore the value of correlation between part-level and global features. In this paper, with a concise structure, RMHNet conducts a uniform partitioning strategy to relocate images into horizontal and vertical stripes. We incorporate the relationship between part-level semantic representation with the entire image to mine structural relationship information of the probe image for potential critical identification clues. Extensive experiments demonstrated the remarkable advantage of our RMHNet to state-of-the-art methods on several public benchmarks includes 84.2% rank-1 accuracy on MSMT17 (Wei et al., in: Internaltional conference on computer vision and pattern recogintion (CVPR), pp 79–88, 2018. https://doi.org/10.1109/CVPR.2018.00016) datasets, which is currently the largest publicly available person re-id dataset [ABSTRACT FROM AUTHOR]