Most recent unsupervised person re-identification methods maintain a cluster uni-proxy for
contrastive learning. However, due to the intra-class variance and inter-class similarity, the
cluster uni-proxy is prone to be biased and confused with similar classes, resulting in the
learned features lacking intra-class compactness and inter-class separation in the
embedding space. To completely and accurately represent the information contained in a
cluster and learn discriminative features, we propose to maintain discrepant cluster proxies …

The cluster contrastive loss weight λ. We analyze the weight λ of cluster contrastive loss
LDCP (Eq. 8) in the overall loss LDCMIP (Eq. 10) on Market-1501 and MSMT17 in Figure 1.
λ controls the proportion of the cluster contrastive loss and instance contrastive loss. When λ
is small and the instance contrastive loss is weighted more heavily, the performance on both
datasets drops significantly, especially for MSMT17. However, when λ is large and the
cluster contrastive loss is weighted more heavily, the model still achieves good performance …