Unsupervised person re-identification aims to retrieve a given pedestrian image from unlabeled data. The method of clustering and assigning pseudo-labels has become mainstream, but there are still some problems that will reduce recognition accuracy. On the one hand, in the process of clustering, poor classification of hard samples between neighboring classes leads to inadequate clustering accuracy, which affects the quality of pseudo-labels. On the other hand, the representational capacity of features extracted by the backbone network is also crucial for the model’s performance. To this end, this paper proposes an unsupervised person re-identification method based on nearest neighbor sample constraint and ordinary differential equation guided feature reconstruction (NNSC-FR) to improve the clustering accuracy and pseudo-label quality while enhancing the representation of features. Specifically, we present a novel nearest neighbor sample constraint (NNSC) after neighbor sample mining for each instance sample to recognize the hard samples’ fine classification between classes. To further improve clustering accuracy, an inter-class balance loss (CB loss) is introduced to better identify the hard samples between the nearest neighbor classes. In addition, guided by the third-order adam solution of the Ordinary Differential Equation, we design a Feature Reconstruction (ODE-FR) module with residual structure to improve the model representation ability. Extensive experimental results on Market-1501, DukeMTMC-reID, and MSMT17 demonstrate that our proposed method is superior to the state-of-the-art methods.

Unsupervised person re-identification aims to retrieve a given pedestrian image from unlabeled data. For training on the unlabeled data, the method of clustering and assigning pseudo-labels has become mainstream, but the pseudo-labels themselves are noisy and will reduce the accuracy. To overcome this problem, several pseudo-label improvement methods have been proposed. But on the one hand, they only use target domain data for fine-tuning and do not make sufficient use of high-quality labeled data in the source domain. On the other hand, they ignore the critical fine-grained features of pedestrians and overfitting problems in the later training period. In this paper, we propose a novel unsupervised cross-domain person re-identification network (IDENet) based on an inter-domain equilibrium structure to improve the quality of pseudo-labels. Specifically, we make full use of both source domain and target domain information and construct a small learning network to equalize label allocation between the two domains. Based on it, we also develop a dynamic neural network with adaptive convolution kernels to generate adaptive residuals for adapting domain-agnostic deep fine-grained features. In addition, we design the network structure based on ordinary differential equations and embed modules to solve the problem of network overfitting. Extensive cross-domain experimental results on Market1501, PersonX, and MSMT17 prove that our proposed method outperforms the state-of-the-art methods.