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Proceedings Paper

Full batch loss for person re-identification
Author(s): Bing Chen; Yufei Zha; Min Wu; Yunqiang Li; Zhiqiang Hou
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Paper Abstract

Distance metric learning between pairs of samples is the key issue for the person re-identification(re-ID). Recently, convolutional neural networks (CNNs) significantly is employed to improve performance by learning deep semantic features which requires a large amount of labeled data for train the deep model. For person re-ID task, the training data is scarce. To the end, how to use the limited data to achieve the optimal deep model is a challenging problem. We observe that sample's similarity information and identity information are complementary. Therefore, a full-batch loss function is proposed in this paper, which promotes the distance relationship of individual sample pairs in a batch to the distance matrix in all samples to make full use of the sample similarity information. At the same time, it also makes full use of the sample's identity information by integrating the identification loss function. We conduct experiments on the two public person re-ID benchmarks: Market1501 and DukeMTMC-reID. The results clearly demonstrate that our proposed full batch loss model produces more discriminative descriptors for person re-ID, which outperforms well established baselines significantly and offer new state-of-the-art performances

Paper Details

Date Published: 6 May 2019
PDF: 11 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690H (6 May 2019); doi: 10.1117/12.2524336
Show Author Affiliations
Bing Chen, Air Force Engineering Univ. (China)
Yufei Zha, Air Force Engineering Univ. (China)
Min Wu, Air Force Engineering Univ. (China)
Yunqiang Li, Air Force Engineering Univ. (China)
Zhiqiang Hou, Air Force Engineering Univ. (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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