With the rise of deep learning technology, person re-identification (Re-id) technology has been developed rapidly. During the training process, many recent methods are susceptible to target misalignment and without sufficient discriminative features. Aiming at these two problems, a simple and potent model is proposed by us. A new self-attention module and a multi-loss function with relative weight are designed to integrate the multi-level features of pedestrians in our network. Specifically, the goal of the self-attention is to instruct the baseline network to learn robust features from the resized images. The key of the self-attention model is the weighting of the importance of different person regions. Therefore, the non-local features of the different levels in the baseline network will be paid more attention, which is conducive to learn discriminative features from the baseline network. Finally, a multi-loss function with relative weight is introduced to enhance the feature learning ability and integrate more features reasonably. Many experiments have been done on the three datasets (Market1501, DukeMTMC-reID and CUHK03-NP) and the results explain that the new model gets a higher accuracy than many other recent approaches.

随着深度学习技术的兴起，人员重新识别（Re-id）技术得到了迅速的发展。在训练过程中，许多最近的方法很容易出现目标未对准且没有足够的区分特征的情况。针对这两个问题，我们提出了一个简单有效的模型。设计了一个新的自我注意模块和具有相对权重的多损失功能，以将行人的多层次功能集成到我们的网络中。具体而言，自我关注的目标是指导基线网络从调整大小后的图像中学习可靠的功能。自我注意模型的关键是对不同人员区域重要性的加权。因此，基线网络中不同级别的非本地功能将受到更多关注，这有利于从基准网络中学习区分特征。最后，引入具有相对权重的多重损失函数，以增强特征学习能力并合理地整合更多特征。在这三个数据集（Market1501，DukeMTMC-reID和CUHK03-NP）上进行了许多实验，结果表明，新模型比许多其他最新方法具有更高的准确性。