  Object re-identification method is made up of backbone network, feature
aggregation, and loss function. However, most backbone networks lack a special
mechanism to handle rich scale variations and mine discriminative feature
representations. In this paper, we firstly design a hierarchical similarity
graph module (HSGM) to reduce the conflict of backbone and re-identification
networks. The designed HSGM builds a rich hierarchical graph to mine the
mapping relationships between global-local and local-local. Secondly, we divide
the feature map along with the spatial and channel directions in each
hierarchical graph. The HSGM applies the spatial features and channel features
extracted from different locations as nodes, respectively, and utilizes the
similarity scores between nodes to construct spatial and channel similarity
graphs. During the learning process of HSGM, we utilize a learnable parameter
to re-optimize the importance of each position, as well as evaluate the
correlation between different nodes. Thirdly, we develop a novel hierarchical
similarity graph network (HSGNet) by embedding the HSGM in the backbone
network. Furthermore, HSGM can be easily embedded into backbone networks of any
depth to improve object re-identification ability. Finally, extensive
experiments on three large-scale object datasets demonstrate that the proposed
HSGNet is superior to state-of-the-art object re-identification approaches.
