Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification

Understand

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 deteriorate 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 by the original clustering result and a weighted ensemble of its neighbours' predictions, with weights determined according to their similarities in the feature space.