【摘要】 针对现有的直接跨数据集的行人重识别方法泛化性不足、跨域能力较差的问题，文中提出逐点特征匹配的跨域行人重识别方法，只需在源域上进行模型训练，在目标域上进行测试，就可达到较好效果.首先，为了解决网络对于跨域的行人图像风格、颜色等鲁棒性不高的问题，在ResNet50基础网络中引入实例归一化层，提取图像特征.然后，利用Transformer的多头自注意力模块与卷积结合，增强特征的表示能力.最后，通过在深层特征中建立一种逐点的特征映射关系，将图像匹配视为逐点寻找局部最优的过程，在未知场景中提升模型的抗视角变化能力，增强模型的泛化性.实验表明，文中方法在提高模型泛化能力上具有一定的优越性.更多还原

【Abstract】 To improve the poor generalization and cross-domain capability of the existing direct cross-dataset person re-identification methods, a cross-domain person re-identification method based on point-by-point feature matching is proposed. By the proposed method, the model only needs to be trained on the source domain and tested on the target domain to achieve better results. Firstly, to improve the poor robustness of the network for style and color of cross-domain pedestrian images, instance normalization layer(IN) is introduced into the ResNet50 basic network to extract image features. Secondly, the multi-head self-attention module of Transformer is combined with convolution to enhance the representation ability of features. Finally, by establishing a point-by-point feature mapping relationship in the deep features, image matching is regarded as a point-by-point process of finding the local optimum to improve the ability of the proposed model to resist perspective changes in unknown scenes and enhance its generalization. The experimental results show that the advantages of the proposed method in improving the generalization ability.更多还原