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• W4361762922 abstract "The past decade has seen the rapid development of deep learning methods in person re-identification. However, most existing models ignore the sharing and exclusive information between different images, leading to insufficient discriminative information from inter-class images. Therefore, this paper proposes a multi-branch graph convolution model to learn the sharing and exclusive information both at the global and local levels. First, using multi-granularity features as graph nodes and combining cosine similarity to construct local and global graphs to mine the relationships between pedestrian images; then, embedding the inter-local and global relationships into the feature representation of pedestrian images by graph convolution operations; finally, combining identity loss and component segmentation loss as the final loss function for the model training. Experimental results on the Market-1501, CUHK03, and DukeMTMC-reID datasets show that the proposed method can effectively improve the performance of person re-identification." @default.
• W4361762922 created "2023-04-04" @default.
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• W4361762922 date "2023-01-01" @default.
• W4361762922 modified "2023-09-25" @default.
• W4361762922 title "Graph-Based Multi-granularity Person Re-identification" @default.
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• W4361762922 doi "https://doi.org/10.1007/978-981-99-0923-0_14" @default.
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