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.

我们提出了一种自适应类内变异对比学习算法AdaInCV，该算法通过考虑聚类后的类内变化量定量评估模型对每个类别的学习能力，从而在模型的训练过程中选择适当的样本。具体而言，我们提出了两种新策略：自适应样本挖掘（AdaSaM）和自适应离群值过滤（AdaOF）。第一个策略逐渐创建更可靠的聚类以动态改进记忆，而第二个策略可以识别和过滤有价值的离群值作为负样本。

自适应类内变异对比学习用于无监督个体再识别