  Unsupervised person re-identification (ReID) aims at learning discriminative
identity features without annotations. Recently, self-supervised contrastive
learning has gained increasing attention for its effectiveness in unsupervised
representation learning. The main idea of instance contrastive learning is to
match a same instance in different augmented views. However, the relationship
between different instances of a same identity has not been explored in
previous methods, leading to sub-optimal ReID performance. To address this
issue, we propose Inter-instance Contrastive Encoding (ICE) that leverages
inter-instance pairwise similarity scores to boost previous class-level
contrastive ReID methods. We first use pairwise similarity ranking as one-hot
hard pseudo labels for hard instance contrast, which aims at reducing
intra-class variance. Then, we use similarity scores as soft pseudo labels to
enhance the consistency between augmented and original views, which makes our
model more robust to augmentation perturbations. Experiments on several
large-scale person ReID datasets validate the effectiveness of our proposed
unsupervised method ICE, which is competitive with even supervised methods.
