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• W4321608678 abstract "Visible-thermal person re-identification (VT-ReID) is an image retrieval task that aims at matching the target pedestrian across the visible and thermal modalities. However, intra-class variations and cross-modality discrepancy degrade the performance of VT-ReID. Recent methods focus on extracting discriminative local features of each modality to alleviate the intra-class variations and cross-modality discrepancy, but these methods ignore semantic relations between the local features of two modalities, <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>i.e.</i> , the spatial relations and channel relations. In this paper, we proposed a feature aggregation module (FAM) to enhance the correlation between local features including spatial dependencies and channel dependencies. Furthermore, FAM implements cross-modality feature aggregation on the enhanced features to reduce the cross-modality discrepancy. Moreover, we also proposed near neighbor cross-modality loss (NNCLoss) to mine feature consistency between modalities by constructing a cross-modality near neighbor set, which facilitates feature alignment between two modalities. Extensive experiments on two datasets demonstrate the superior performance of our approach over the existing state-of-the-arts." @default.
• W4321608678 created "2023-02-24" @default.
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• W4321608678 date "2023-01-01" @default.
• W4321608678 modified "2023-10-16" @default.
• W4321608678 title "Feature Aggregation via Attention Mechanism for Visible-Thermal Person Re-Identification" @default.
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• W4321608678 doi "https://doi.org/10.1109/lsp.2023.3244747" @default.
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