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Beyond triplet loss: a deep quadruplet network for person re-identification
Weihua Chen1; Xiaotang Chen1; Jianguo Zhang2; Kaiqi Huang1
Conference NameIEEE Computer Vision and Pattern Recognition (CVPR)
Conference Date2017-7-21
Conference PlaceHawaii, USA
AbstractPerson re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.
Indexed ByEI
Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Weihua Chen,Xiaotang Chen,Jianguo Zhang,et al. Beyond triplet loss: a deep quadruplet network for person re-identification[C],2017.
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