  Contrastive self-supervised learning (CSL) based on instance discrimination
typically attracts positive samples while repelling negatives to learn
representations with pre-defined binary self-supervision. However, vanilla CSL
is inadequate in modeling sophisticated instance relations, limiting the
learned model to retain fine semantic structure. On the one hand, samples with
the same semantic category are inevitably pushed away as negatives. On the
other hand, differences among samples cannot be captured. In this paper, we
present relation-aware contrastive self-supervised learning (ReCo) to integrate
instance relations, i.e., global distribution relation and local interpolation
relation, into the CSL framework in a plug-and-play fashion. Specifically, we
align similarity distributions calculated between the positive anchor views and
the negatives at the global level to exploit diverse similarity relations among
instances. Local-level interpolation consistency between the pixel space and
the feature space is applied to quantitatively model the feature differences of
samples with distinct apparent similarities. Through explicitly instance
relation modeling, our ReCo avoids irrationally pushing away semantically
identical samples and carves a well-structured feature space. Extensive
experiments conducted on commonly used benchmarks justify that our ReCo
consistently gains remarkable performance improvements.
