Abstract
Person re-identification technology has made significant progress in recent years with the development of deep learning. However, the recognition rate of models in this field is still lower than that of face recognition, which is challenging to implement in practical application scenarios. Therefore, improving the recognition rate of the pedestrian re-identification model is still a critical task. This paper mainly focuses on three aspects of this problem. The first is to use the characteristics of the multi-branch network structure of person re-identification to dig out the most effective online self-distillation scheme between branches without increasing additional resource requirements, making full use of the information contained in each branch. Secondly, this paper analyzes and verifies the pros and cons of knowledge distillation based on mean squared error (MSE) loss function and Kullback-Leibler (KL) divergence from theoretical and experimental perspectives. Finally, we verified through experiments that adding a specific value of noise perturbation to the model weights can further improve the recognition rate of the model. After several improvements in these areas, we obtained the current state-of-the-art performance on four public datasets for person re-identification.