  This work aims at improving instance retrieval with self-supervision. We find
that fine-tuning using the recently developed self-supervised (SSL) learning
methods, such as SimCLR and MoCo, fails to improve the performance of instance
retrieval. In this work, we identify that the learnt representations for
instance retrieval should be invariant to large variations in viewpoint and
background etc., whereas self-augmented positives applied by the current SSL
methods can not provide strong enough signals for learning robust
instance-level representations. To overcome this problem, we propose InsCLR, a
new SSL method that builds on the \textit{instance-level} contrast, to learn
the intra-class invariance by dynamically mining meaningful pseudo positive
samples from both mini-batches and a memory bank during training. Extensive
experiments demonstrate that InsCLR achieves similar or even better performance
than the state-of-the-art SSL methods on instance retrieval. Code is available
at https://github.com/zeludeng/insclr.
