## 2 Related Works
### Weakly Supervised Pedestrian Re-Identification Research
#### 2.3.2 Unsupervised Re-ID  
Unsupervised methods mainly focus on domain adversarial learning or constructing pseudo-labels using unsupervised clustering. However, these methods have some limitations. Firstly, they do not take into account the Intra-Domain Variations (IDVs) within the target domain, and the features of the same pedestrian in different states can vary considerably. Secondly, these methods focus more on the current features rather than the global features, which may have a large impact on the early stage of training. Early unsupervised pedestrian re-identification methods mainly focused on learning invariant components such as dictionaries, metrics, or saliency, but this led to limitations in discriminability or scalability. Therefore, further research and improvement of unsupervised methods are needed to overcome these limitations and improve the performance and reliability of pedestrian re-identification. The literature [43] proposes a soft multi-label deep learning algorithm for unsupervised pedestrian re-identification, which learns the soft multi-label of each unlabeled pedestrian by comparing the unlabeled person with a set of known reference pedestrians and utilizes the soft multi-labels to address the learning of discriminative information in the absence of pairs of labels in the disjoint camera views. The literature [44] develops a PatchNet to select the portion of interest from the feature map and learn the discriminative features, instead of learning the discriminative features from the whole image, and designs the image-level feature learning loss to utilize all the patch features of the same image as its image-level guide. For deep unsupervised methods [45], cross-camera label estimation is a popular approach. Dynamic Graph Matching (DGM) formulates label estimation as a bipartite graph matching problem. The literature [46] introduces Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) for the simultaneous learning of attribute semantics and the identity discriminative feature representation space,enabling unsupervised learning in the target domain. For end-to-end unsupervised Re-ID, iterative clustering and Re-ID model learning are proposed. Similarly, the relationships between the samples are used in a hierarchical clustering framework. Soft multi-label learning mines soft-label information from reference sets for unsupervised learning.  
Despite the progress of unsupervised cross-domain pedestrian re-identification techniques, their recognition results on publicly available Re-ID datasets are still significantly different from the supervised methods. Future research will focus on improving the performance of unsupervised methods, including exploring new feature representations, improving unsupervised algorithms, and optimizing the unsupervised learning process. In addition, research is needed to investigate how to apply these techniques more efficiently to video surveillance, intelligent security, and other fields, to realize the practical application and promotion of unsupervised cross-domain pedestrian re-identification techniques.