Sparse High-Level Attention Networks for Person Re-Identification

When extracting convolutional features from person images with low resolution, a large amount of available information will be lost due to the pooling, which will lead to the reduction of the accuracy of person classification models. This paper proposes a new classification model, which can effectively to reduce the loss of important information about the convolutional neural works. Firstly, the SE module in the Squeeze-and-Excitation Networks (SENet) is extracted and normalized to generate the Normalized Squeeze-and-Excitation (NSE) module. Then, 4 NSE modules are applied to the convolutional layers of ResNet. Finally, a Sparse Normalized Squeeze-and-Excitation Network (SNSENet) is constructed by adding 4 shortcut connections between the convolutional layers. The experimental results of Market-1501 show that the rank-1 of SNSE-ResNet-50 is 3.7% and 4.2% higher than that of SE-ResNet-50 and ResNet-50 respectively, it has done well in other person re-identification datasets.