In this paper, we propose a robust structural metric learning model for person re-identification with two main advantages: 1) it applies loss functions at the ...
In this paper, we propose a robust structural metric learning model for person re-identification with two main advantages: 1) it applies loss functions at the ...
Gang Yuan, Zhaoxiang Zhang, Yunhong Wang: Enhancing Person Re-identification by Robust Structural Metric Learning. ICIG 2013: 453-458.
This paper proposes a new structured loss function to push the frontier of the person re-ID performance in realistic scenarios.
Generally speaking, person re-ID involves two sub-problems: feature representation and metric learning. An effective feature representation [2], [3] is critical ...
Our results show quantitatively that, the powerful expression of deep features reduces the influence of metric learning on overall performance.
In recent years, deep learning has shown great information processing ability and better robustness, which does not rely on the accurate design of manual ...
By using a. “siamese” deep neural network, the proposed method can jointly learn the color feature, texture feature and metric in a unified framework. The ...
Aug 9, 2024 · The goal of metric learning is to transform the input space to a new space where similarity between vectors of the same class is high and vice ...
In this work, we improve the robustness of open-world re-ID models by proposing a generative metric learning approach to generate adversarial examples.
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