跨模态行人重识别的对称网络算法

Cross-modality person re-identification algorithm using symmetric network

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效性。

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.