To address the problem that the global feature extraction ability of single-branch network is low and does not have the sensitivity of specific scenes due to the interference of factors such as occlusion caused by fixed camera shooting and scale scaling caused by vehicle driving from far and near in vehicle re-identification, ML Net (Mutual Learning Net) is proposed. Firstly, the multi-branch structure of the network consists of one master branch and three slave branches, the master branch is responsible for the feature learning task in general complex scenes, and the three slave branches are responsible for the feature learning task under image occlusion, scale scaling, and vehicle color change, so that the network can be sensitive to specific scenes. Finally, the models are trained in each branching stage jointly with Label Smoothing Cross Entropy Loss, Triplet Loss and KL (Kullback-Leibler Divergence) Loss. The experimental results show that the proposed ML Net network achieves advanced experimental results on two publicly available datasets, VeRi776 and VehicleID.

Xin Bai, Ming Gao, Xin Zheng. Multi-branch mutual learning net for vehicle re-identification. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 11: 46-51. https://doi.org/10.25236/AJCIS.2023.061107.

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