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• W2578137651 abstract "In this paper, we propose an end-to-end deep correspondence structure learning (DCSL) approach to address the cross-camera person-matching problem in the person re-identification task. The proposed DCSL approach captures the intrinsic structural information on persons by learning a semantics-aware image representation based on convolutional neural networks, which adaptively learns discriminative features for person identification. Furthermore, the proposed DCSL approach seeks to adaptively learn a hierarchical data-driven feature matching function which outputs the matching correspondence results between the learned semantics-aware image representations for a person pair. Finally, we set up a unified end-to-end deep learning scheme to jointly optimize the processes of semantics-aware image representation learning and cross-person correspondence structure learning, leading to more reliable and robust person re-identification results in complicated scenarios. Experimental results on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art approaches." @default.
• W2578137651 created "2017-01-26" @default.
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• W2578137651 date "2016-07-09" @default.
• W2578137651 modified "2023-09-28" @default.
• W2578137651 title "Semantics-aware deep correspondence structure learning for robust person re-identification" @default.
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