Transfer easy to hard: Adversarial contrastive feature learning for unsupervised person re-identification

Abstract

Unsupervised Person Re-Identification (Re-ID) is challenging due to the lack of ground-truth labels. Most existing methods address this problem by progressively mining high-confidence pseudo labels to guide the feature learning process. However, how to construct hard-enough samples while maintaining the fidelity of pseudo labels in these samples remains an open issue in the machine learning community. To tackle this challenge, we design a simple yet effective adversarial contrastive feature learning (ACFL) framework, which enhances the discriminative capability of features by introducing more transformed hard samples in the feature learning process. Specifically, it mainly consists of a discriminative feature learning module and a hard sample generation module. The discriminative feature learning module extracts recognizable features of unlabeled training samples to estimate the high-confidence relationship between samples. Then, the hard sample generation module utilizes these high-confidence relationships between samples to transfer all samples into the hard ones via an adversarial learning strategy. Finally, the generated hard samples are further fed into DFL to learn discriminative features for person Re-ID. Extensive experiments on Market-1501, DukeMTMC-reID, and MSMT17 datasets show that our method compares favorably with state-of-the-art methods.