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跨模态行人重识别的对称网络算法
引用本文:张艳,相旭,唐俊,王年,屈磊.跨模态行人重识别的对称网络算法[J].国防科技大学学报,2022,44(1):122-128.
作者姓名:张艳  相旭  唐俊  王年  屈磊
作者单位:安徽大学 电子信息工程学院, 安徽 合肥 230601
基金项目:国家自然科学基金资助项目(61772032,61871411);国家重点研发计划资助项目(2018YFC0807302)
摘    要:针对模态间差异,提出基于对称网络的跨模态行人重识别算法,该网络将基于概率分布的模态混淆与对抗学习结合,通过对称网络产生模态不变特征,从而达到模态混淆的目的;针对外观差异和模态内差异,使用不同隐藏层的网络卷积特征构造混合三元损失,提高网络的特征表征能力.RegDB和SYSU-MM01数据集上的大量实验结果表明了该方法的有...

关 键 词:跨模态  行人重识别  对称网络  对抗学习  混合三元损失
收稿时间:2020/7/22 0:00:00

Cross-modality person re-identification algorithm using symmetric network
ZHANG Yan,XIANG Xu,TANG Jun,WANG Nian,QU Lei.Cross-modality person re-identification algorithm using symmetric network[J].Journal of National University of Defense Technology,2022,44(1):122-128.
Authors:ZHANG Yan  XIANG Xu  TANG Jun  WANG Nian  QU Lei
Institution:School of Electronic and Information Engineering, Anhui University, Hefei 230601, China
Abstract:For the difference between modalities, a cross-modality person re-identification algorithm which based on symmetric network was proposed. The network combined the modal confusion based on probability distribution with adversarial learning, and generated modal-invariant features through symmetric network to achieve modal confusion. To deal with appearance differences and intra-modality differences, the network constructed a mixed-triplet loss using convolution features of different hidden layers, which can improve the characterization capability of the network. Numerous experimental results on the RegDB and SYSU-MM01 datasets demonstrate the effectiveness of the method.
Keywords:cross-modality  person re-identification  symmetric network  adversarial learning  mixed-triplet loss
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