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Inicio  /  Algorithms  /  Vol: 14 Par: 12 (2021)  /  Artículo
ARTÍCULO
TITULO

A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer

Chengyan Zhong    
Guanqiu Qi    
Neal Mazur    
Sarbani Banerjee    
Devanshi Malaviya and Gang Hu    

Resumen

Due to the variation in the image capturing process, the difference between source and target sets causes a challenge in unsupervised domain adaptation (UDA) on person re-identification (re-ID). Given a labeled source training set and an unlabeled target training set, this paper focuses on improving the generalization ability of the re-ID model on the target testing set. The proposed method enforces two properties at the same time: (1) camera invariance is achieved through the positive learning formed by unlabeled target images and their camera style transfer counterparts; and (2) the robustness of the backbone network feature extraction is improved, and the accuracy of feature extraction is enhanced by adding a position-channel dual attention mechanism. The proposed network model uses a classic dual-stream network. Comparative experimental results on three public benchmarks prove the superiority of the proposed method.

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