Unsupervised Person Re-identification using Adversarial Attack Examples and Multi-view Clustering

Unsupervised person re-identification aims at identifying the same person across disparate camera feeds without pre-labeled data. Common approaches employ pseudo-label generation to annotate unlabeled datasets, treating them as if they were accurately labeled. However, these methods often rely on single network pre-trained on datasets with a domain gap or irrelevance, leading to suboptimal feature extraction for the re-identification task. To overcome this, we propose an innovative approach that integrates multiple networks, which can mitigate these drawbacks by capturing a broader range of features. Our strategy involves a multi-view clustering technique that leverages features from several networks to improve label accuracy. Additionally, we introduce adversarial examples coupled with a specific adversarial loss to encourage diverse yet complementary learning across the networks. Our methodology was validated on two extensive re-identification datasets, demonstrating its effectiveness through various network combinations.