| 跨模态行人重识别的对称网络算法 |
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| 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. DOI: 10.11887/j.cn.202201018 |
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| 作者姓名： | 张艳 相旭 唐俊 王年 屈磊 |
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| 作者单位： | 安徽大学 电子信息工程学院, 安徽 合肥 230601 |
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| 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) |
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| 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...
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| 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： | 2020-07-22 |
Cross-modality person re-identification algorithm using symmetric network |
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| 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. DOI: 10.11887/j.cn.202201018 |
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| Authors: | ZHANG Yan XIANG Xu TANG Jun WANG Nian QU Lei |
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| Affiliation: | School of Electronic and Information Engineering, Anhui University, Hefei 230601, China |
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| 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. |
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| Keywords: | cross-modality person re-identification symmetric network adversarial learning mixed-triplet loss |
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