针对不同类型的注意机制堆叠在行人重识别任务中识别效率过低，且过于侧重提取单一粒度的信息导致很难扩 展到真实世界的应用场景等问题，提出了一种基于局部注意力的细粒度模块，将获取的粗粒度特征表示分割成多个部分，引导 网络去提取更细粒度的局部线索。此外，考虑到单一分支在提取多粒度线索方面的不足，设计了一个多分支的细粒度网络，以 进一步提取更丰富的线索。在主流数据集上的实验结果验证了文中所提网络的有效性。

To solve the problem that the recognition efficiency of different types of attention mechanisms is too low in the person reidentification task, and the focus is too much on extracting single-granularity information, which makes it difficult to extend to realworld application scenarios, a fine-grained module based on local attention is proposed. The obtained coarse-grained feature repre‐ sentation is divided into multiple parts, guiding the network to extract finer-grained local clues. In addition, considering the short‐ comings of a single branch in extracting multi-granularity clues, a multi-branch fine-grained network is designed to further extract richer clues. Experimental results on mainstream datasets verify the effectiveness of the network proposed in this article