Adaptive Intra-Class Variation Contrastive Learning for Unsupervised Person Re-Identification

arXiv:2404.04665v1 Announce Type: new
Abstract: The memory dictionary-based contrastive learning method has achieved remarkable results in the field of unsupervised person Re-ID. However, The method of updating memory based on all samples does not fully utilize the hardest sample to improve the generalization ability of the model, and the method based on hardest sample mining will inevitably introduce false-positive samples that are incorrectly clustered in the early stages of the model. Clustering-based methods usually discard a significant number of outliers, …

abstract arxiv class cs.ai cs.cv dictionary however identification memory person results sample samples type unsupervised variation