Cluster Contrast for Unsupervised Person Re-Identification

Understand

State-of-the-art unsupervised re-ID methods train the neural networks using a memory-based non-parametric softmax loss.

• Instance feature vectors stored in memory are assigned pseudo-labels by clustering and updated at instance level.
• However, the varying cluster sizes leads to inconsistency in the updating progress of each cluster.
• To solve this problem, we present Cluster Contrast which stores feature vectors and computes contrast loss at the cluster level.