In person re-identification (Re-ID) task, multi-branch networks acquire better performance by combining global features and local features. Obviously, local branch can obtain detailed information of person pictures but may work on invalid regions when person pictures have imprecise bounding boxes. On the contrary, global branch can be aware of the position of person but hard to acquire detailed information of person pictures. Meanwhile, lots of multi-branch networks ignore mutual information among different branches. Therefore, it is necessary to enhance interaction of global branch and local branch. For this purpose, we propose Interactive Information Module (IIM). IIM includes two components named Global-map Attention Module (GAM) and Labeled-class Mutual Learning (LML), respectively. GAM leverages heatmaps generated by global branch to guide calculation of local attention and obtains a composite global feature by combining local features. GAM relys more on the performance of global branch which decides the quality of heatmaps. To improve performance of global branch, we propose LML to promote convergent rate of global branch. Extensive experiments implemented on Market-1501, DukeMTMC-ReID, and CUHK03-NP datasets confirm that our method achieves state-of-the-art results.

在人员重新识别（Re-ID）任务中，多分支网络通过结合全局功能和本地功能获得更好的性能。显然，本地分支可以获得人像的详细信息，但是当人像的边界框不精确时，可能会在无效区域上工作。相反，全球分支机构可以知道人物的位置，但是很难获取人物图片的详细信息。同时，许多多分支网络忽略了不同分支之间的相互信息。因此，有必要加强全球分支机构与本地分支机构的互动。为此，我们提出了交互式信息模块（IIM）。IIM包括两个组件，分别称为全局地图注意模块（GAM）和标签类互学习（LML）。GAM利用全局分支生成的热图来指导局部关注度的计算，并通过组合局部特征获得复合的全局特征。GAM更加依赖于决定热图质量的全球分支机构的绩效。为了提高全球分支机构的绩效，我们提出LML来提高全球分支机构的收敛速度。在Market-1501，DukeMTMC-ReID和CUHK03-NP数据集上进行的广泛实验证实了我们的方法获得了最新的结果。