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.

翻译：物体再识别方法由主干网、特征聚合和丢失功能组成。然而,大多数主干网缺乏处理大型变异和地雷差别特征表示的特殊机制。在本文件中,我们首先设计了一个等级相似图形模块(HSGM)以减少主干网和再识别网络的冲突。设计HSGM的等级图丰富,以挖掘全球-地方和地方-地方之间的绘图关系。第二,我们将地貌图与每个等级图的空间和频道方向分开。HSGM将不同地点的空间特征和频道特征分别作为节点加以应用,并利用节点之间的相似分来构建空间和频道相似图形。在HSGM的学习过程中,我们使用一个可学习的参数来重新优化每个位置的重要性,并评估不同节点之间的相互关系。第三,我们通过将HSGM网络嵌入主干网,开发一个新的等级相似图形网络。此外,HSGMGM可以很容易嵌入任何深度的主干网,利用节点之间的类似分分分来构建空间和频道图。在HSGMGM的学习过程中,我们利用一个可学习的参数重新定位能力重新定位的参数,最后,在高级SGNet上进行大规模实验,以显示高级定位的3级数据定位。