Adaptive Intra-Class Variation Contrastive Learning for Unsupervised
  Person Re-Identification
Lingzhi Liu Haiyang Zhang Chengwei Tang Tiantian Zhang
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,
leading to the loss of valuable information. In order to address the issues
mentioned before, we propose an adaptive intra-class variation contrastive
learning algorithm for unsupervised Re-ID, called AdaInCV. And the algorithm
quantitatively evaluates the learning ability of the model for each class by
considering the intra-class variations after clustering, which helps in
selecting appropriate samples during the training process of the model. To be
more specific, two new strategies are proposed: Adaptive Sample Mining (AdaSaM)
and Adaptive Outlier Filter (AdaOF). The first one gradually creates more
reliable clusters to dynamically refine the memory, while the second can
identify and filter out valuable outliers as negative samples.