Reliable Exploration Strategy for Unsupervised Person Re-Identification

Unsupervised person re-identification addresses the task of associating individuals across disjoint camera views in the absence of annotated training data. While current methods often focus on hard samples to learn discriminative features, these hard samples are more prone to label noise, which can negatively impact model performance. To address this issue, we propose an information-complementary hybrid contrastive learning framework that consists of two branches for extracting richer semantic information. Specifically, the first branch performs cluster-level contrastive learning to capture general semantic patterns, while the second branch employs relation-guided instance-level contrastive learning, which leverages local structures to mine hard samples for more discriminative representations. To mitigate label noise during hard sample mining, we introduce a temporal-guided dynamic weighting module that uses temporal clustering results to evaluate the reliability of the mined hard samples. This module generates instance-level weights to reduce the impact of unreliable samples, ensuring more stable training. Extensive experiments on four popular benchmarks demonstrate the superiority of our method compared to state-of-the-art approaches.