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Unsupervised person re‐identification has attracted vital interest because of its ability to protect privacy, significantly lower the expense of manual annotation, and eliminate the need for data labels. General unsupervised methods train the network only through global features, which causes the fine‐grained information contained in local features to be ignored in the recognition process, resulting in large amounts of label noise and affecting the recognition accuracy. Moreover, more robust pedestrian features can also improve the accuracy of clustering and enable unsupervised person re‐identification to obtain better results. To address these issues, first, a dual‐branch structure was proposed, which separately obtains the global features of the pedestrian and the local features by dividing the global features into a few equal sections. Then, an adaptive information supplementation (AIS) method based on the k‐nearest neighbor algorithm is designed to ascertain each local feature's relevance to the global features, calculating adaptive weight scores for information supplementation. Finally, these weight scores are used to reallocate the weights of the global features in each part, acquiring features that contain more pedestrian information during the representation learning process. These better features are used to reduce label noise to obtain more accurate pseudo‐labels. Second, an adaptive foreground enhancement module (AFEM) was proposed and inserted before clustering to increase the robustness of pedestrian features, which increases the precision of the pseudo‐labels that are produced after clustering. Experiments on Market‐1501, DukeMTMC‐reID, and MSMT17 demonstrate that the proposed method achieves better results than state‐of‐the‐art methods in fully unsupervised person re‐identification tasks. [ABSTRACT FROM AUTHOR]

The visible-infrared person re-identification (VI-ReID) task aims to retrieve the same pedestrian between visible and infrared images. VI-ReID is a challenging task due to the huge modality discrepancy and complex intra-modality variations. Existing works mainly complete the modality alignment at one stage. However, aligning modalities at different stages has positive effects on the intra-class and inter-class distances of cross-modality features, which are often ignored. Moreover, discriminative features with identity information may be corrupted in the processing of modality alignment, further degrading the performance of person re-identification. In this paper, we propose a progressive discriminative feature learning (PDFL) network that adopts different alignment strategies at different stages to alleviate the discrepancy and learn discriminative features progressively. Specifically, we first design an adaptive cross fusion module (ACFM) to learn the identity-relevant features via modality alignment with channel-level attention. For well preserving identity information, we propose a dual-attention-guided instance normalization module (DINM), which can well guide instance normalization to align two modalities into a unified feature space through channel and spatial information embedding. Finally, we generate multiple part features of a person to mine subtle differences. Multi-loss optimization is imposed during the training process for more effective learning supervision. Extensive experiments on the public datasets of SYSU-MM01 and RegDB validate that our proposed method performs favorably against most state-of-the-art methods. [ABSTRACT FROM AUTHOR]