A Two-Stage Noise-Tolerant Paradigm for Label Corrupted Person Re-Identification

Supervised person re-identification (Re-ID) approaches are sensitive to label corrupted data, which is inevitable and generally ignored in the field of person Re-ID. In this paper, we propose a two-stage noise-tolerant paradigm (TSNT) for labeling corrupted person Re-ID. Specifically, at stage one, we present a self-refining strategy to separately train each network in TSNT by concentrating more on pure samples. These pure samples are progressively refurbished via mining the consistency between annotations and predictions. To enhance the tolerance of TSNT to noisy labels, at stage two, we employ a co-training strategy to collaboratively supervise the learning of the two networks. Concretely, a rectified cross-entropy loss is proposed to learn the mutual information from the peer network by assigning large weights to the refurbished reliable samples. Moreover, a noise-robust triplet loss is formulated for further improving the robustness of TSNT by increasing inter-class distances and reducing intra-class distances in the label-corrupted dataset, where a constraint condition for reliability discrimination is carefully designed to select reliable triplets. Extensive experiments demonstrate the superiority of TSNT, for instance, on the Market1501 dataset, our paradigm achieves 90.3% rank-1 accuracy (6.2% improvement over the state-of-the-art method) under noise ratio 20%.