  Recently, cluster contrastive learning has been proven effective for object
ReID by computing the contrastive loss between the individual features and the
cluster memory. However, existing methods that use the individual features to
momentum update the cluster memory will fluctuate over the training examples,
especially for the outlier samples. Unlike the individual-based updating
mechanism, the centroid-based updating mechanism that applies the mean feature
of each cluster to update the cluster memory can reduce the impact of
individual samples. Therefore, we formulate the individual-based updating and
centroid-based updating mechanisms in a unified cluster contrastive framework,
named Dual Cluster Contrastive framework (DCC), which maintains two types of
memory banks: individual and centroid cluster memory banks. Significantly, the
individual cluster memory considers just one individual at a time to take a
single step for updating. The centroid cluster memory applies the mean feature
of each cluster to update the corresponding cluster memory. During
optimization, besides the vallina contrastive loss of each memory, a cross-view
consistency constraint is applied to exchange the benefits of two memories for
generating a discriminative description for the object ReID. Note that DCC can
be easily applied for unsupervised or supervised object ReID by using
ground-truth labels or the generated pseudo-labels. Extensive experiments on
three benchmarks, \emph{e.g.,} Market-1501, MSMT17, and VeRi-776, under
\textbf{supervised Object ReID} and \textbf{unsupervised Object ReID}
demonstrate the superiority of the proposed DCC.
