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| A Fine-grained Network for Person Re-identification |
| Nanjing Audit University |
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Abstract 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
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Published: 02 July 2024
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