Neighbor Consistency and Global-Local Interaction: A Novel Pseudo-label Refinement Approach for Unsupervised Person Re-Identification

Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labels, but these pseudo labels are inevitably noisy, which deteriorates model performance. In this paper, we propose a Neighbour Consistency guided Pseudo Label Refinement (NCPLR) framework, which can be regarded as a transductive form of label propagation under the assumption that the prediction of each example should be similar to its nearest neighbours’. Specifically, the refined label for each training instance can be obtained from the original clustering result and a weighted ensemble of its neighbours’ predictions, with weights determined according to their similarities in the feature space. Furthermore, we also explore building a unified global-local NCPLR mechanism through a global-local label interaction module to achieve mutual label refinement. Such a strategy promotes efficient complementary learning while mitigating some unreliable information, finally improving the quality of the refined pseudo labels for each global-local region. Extensive experimental results demonstrate the effectiveness of the proposed method, showing superior performance to state-of-the-art methods by a large margin. Our source code is released in https://github.com/haichuntai/NCPLR-ReID.