The devil in the tail: Cluster consolidation plus cluster adaptive balancing loss for unsupervised person re-identification

Highlights：

• We propose a simple yet effective approach, called cluster consolidation (CC), to reorganize the clustering result. The reorganization step can improve the compactness of larger clusters by pruning a proportion of unreliable samples into tiny clusters or singletons.

• We propose a cluster adaptive balancing (CAB) loss to effectively train the network by automatically assigning proper weights to the imbalanced and noisy pseudo labels. In this way, the unsupervised person Re-ID task is formulated as a cluster adaptive long-tail learning problem.

• Extensive experiments on widely used benchmark datasets are conducted and demonstrate state-of-the-art performance. A set of ablation studies are also provided.