基于多尺度和注意力融合学习的行人重识别

行人重识别的关键依赖于行人特征的提取，卷积神经网络具有强大的特征提取以及表达能力。针对不同尺度下可以观察到不同的特征，该文提出一种基于多尺度和注意力网络融合的行人重识别方法(MSAN)。该方法通过对网络不同深度的特征进行采样，将采样的特征融合后对行人进行预测。不同深度的特征图具有不同的表达能力，使网络可以学习到行人身上更加细粒度的特征。同时将注意力模块嵌入到残差网络中，使得网络能更加关注于一些关键信息，增强网络特征学习能力。所提方法在Market1501, DukeMTMC-reID和MSMT17_V1数据集上首位准确率分别到了95.3%, 89.8%和82.2%。实验表明，该方法充分利用了网络不同深度的信息和关注的关键信息，使模型具有很强的判别能力，而且所提模型的平均准确率优于大多数先进算法。

Person Re-identification Based on Multi-scale Network Attention Fusion

The key to person re-identification depends on the extraction of pedestrian characteristics. Convolutional neural networks have powerful feature extraction and expression capabilities. In view of the fact that different features can be observed at different scales, a pedestrian re-identification method based on Multi-Scale Attention Network(MSAN) fusion is proposed. This method samples the features at different depths of the network and fuses the sampled features to predict pedestrians. Feature maps of different depths have different expressive powers, enabling the network to learn more fine-grained features of pedestrians. At the same time, the attention module is embedded in the residual network, so that the network can pay more attention to some key information and enhance the network feature learning ability. The accuracy of the proposed method on the datasets such as Market1501, DukeMTMC-reID and MSMT17_V1 reaches 95.3%, 89.8% and 82.2%, respectively. Experiments show that the method makes full use of the information of different depths of the network and the key information of interest, so that the model has strong discriminating ability, and the average accuracy of the proposed model is better than most state-of-the-art algorithms.