软伪标签和多尺度特征融合的行人重识别

Soft Pseudo-Label and Multi-Scale Feature Fusion for Person Re-Identification

传统无监督域自适应行人重识别算法,抑制伪标签噪声效果差、域间泛化能力弱。针对这些问题,提出了一种基于软伪标签和多尺度特征融合的无监督域自适应行人重识别算法。为抑制伪标签噪声,利用并行网络的预测值作为软标签,通过交叉校对方式对伪标签噪声进行纠偏,为无监督域自适应任务提供更鲁棒的软伪标签。为增强域间泛化能力,利用多尺度特征重构和哈达玛积特征融合方法对深浅特征层信息进行处理,实现源域数据到目标域的风格转换,并结合实例和批量归一化网络解决残差网络域自适应性差的问题,增强网络对源域和目标域的泛化能力。实验结果表明,所提算法在Market-to-Duke和Duke-to-Market无监督域自适应任务中都取得了较好的性能,明显优于相关算法。

The traditional unsupervised domain adaptive person re-identification algorithm suppressed the noise of pseudolabel poorly and lack inter-domain generalization ability.For the above problems,an unsupervised domain adaptive person re-identification algorithm was proposed which based on soft pseudo-label and multi-scale feature reconstruction.In order to suppress pseudo-label noise,the predicted value of the parallel network is used as the soft tag,and pseudo-label noise is corrected by cross-proofreading methods,which provides a more robust soft false tag for unsupervised domain adaptive tasks.In order to enhance the generalization ability between domains,multi-scale feature reconstruction and Hadamard product feature fusion methods are used to process the deep and shallow feature layer information,realize the style conversion from source domain data to target domain,and solve the problem of poor adaptability of residual network domain with instance normalization and batch normalization network,so as to enhance the generalization ability of the network to source domain and target domain.Experimental results show that the proposed algorithm has achieved good performance in both Market to Duke and Duke to Market unsupervised domain adaptive tasks,which is significantly better than the related algorithms.