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
