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%.

Some unsupervised approaches have been proposed recently for the person re-identification (ReID) problem since annotations of samples across cameras are time-consuming. However, most of these methods focus on the appearance content of the sample itself, and thus seldom take the structure relations among samples into account when learning the feature representation, which would provide a valuable guide for learning the representations of the samples. Thus hard samples may not be well solved due to the limited or even misleading information of the sample itself. To address this issue, in this article, we propose a Relation-Preserving Feature Embedding (RPE) model that leverages structure relations among samples to boost the performance of the unsupervised person ReID methods without requiring any sample annotations. RPE aims at integrating the sample content and the neighborhood structure relations among samples into the learning of feature embeddings by combining the advantages of the autoencoder and graph autoencoder. Specifically, a relation and content information fusion (RCIF) module is proposed to dynamically merge the information from both perspectives of content and relation levels for feature embedding learning. Also, due to the lack of the identity labels of samples, we adopt an adaptive optimization strategy to update the affinity relations among samples instead of the reconstruction of the whole affinity matrix for optimizing the RPE model, which is more suitable for the unsupervised ReID task. Rigorous experiments on three widely-used large-scale benchmarks for person ReID demonstrate the superiority of the proposed method over current state-of-the-art unsupervised methods.

Most person re-identification (Re-ID) approaches rely excessively on a great quantity of annotated training data. However, due to sampling errors or annotated errors, the label noise is unavoidable, which usually causes a dramatic decrease in the performance of existing Re-ID methods. To address this problem, we propose the label reliability perception (LRP) for person Re-ID by refining noisy labels. Specifically, a feature-fusion block (FFB) is proposed to enhance the discrimen- ability of pedestrians' features by expanding the network's attention span due to the fused feature, which is generated by overlapping the coarse-grained feature obtained by global average pooling and fine-grained features obtained by evenly dividing the feature map in the height dimension and performing global max pooling. In addition, the label dual perception (LDP) is proposed to refine noisy labels instead of filtering samples by evaluating the reliability of each training sample's label. Specifically, we meticulously design five evaluation modes for each sample to perceive the reliability of the labels of thek-nearest neighbor images. Finally, we utilize the most reliable label to replace the noisy label and optimize the network. Extensive experiments prove the superiority of the proposed model over the competing methods; for instance, on Market1501, our method achieves 88.8% rank-1 accuracy and 70.5% mAP (4.7% and 4.3% improvements over the state-of-the-arts) under noise ratio 20%, and similarly on DukeMTMC-ReID, our method achieves 77.7% and 60.3%.