Review of Unsupervised Domain Adaptive Person Re-identification Based on Pseudo-labels
Person re-identification is one of the hot research topics in the field of computer vision.In recent years,in order to solve the problem of scarcity of label data in the practical application of person re-identification,and to effectively use the existing label data,researchers have proposed domain adaptive methods based on generative adversarial networks and pseudo-labels to carry out cross-domain person re-identification research.The unsupervised domain adaptive person re-identification method based on pseu-do-labels is favored by researchers due to its remarkable effect.This paper sorts out the work of pseudo-label-based adaptive per-son re-identification in the unsupervised field in the past 7 years,and divides the pseudo-label-based method into two stages from the perspective of model training:1)Pseudo-label generation stage.Most of the pseudo-label generation methods in existing works use clustering methods,and some works use graph matching based on graph structure learning and graph neural network methods to generate pseudo-labels in the target domain.2)Pseudo-label refining stage.In this paper,the existing pseudo-label refinement methods are summarized into the refinement method based on representation learning and the refinement method based on simi-larity learning,and the model methods are summarized and organized respectively.Finally,the current challenges of pseudo-label-based adaptive person re-identification in the unsupervised domain are discussed and the possible future development directions are prospected.
Person re-edentificationDeep learningPseudo-labelUnsupervised learningDomain adaptation
NETL
NSTL
万方数据
