  In vision domain, large-scale natural datasets typically exhibit long-tailed
distribution which has large class imbalance between head and tail classes.
This distribution poses difficulty in learning good representations for tail
classes. Recent developments have shown good long-tailed model can be learnt by
decoupling the training into representation learning and classifier balancing.
However, these works pay insufficient consideration on the long-tailed effect
on representation learning. In this work, we propose interpolative centroid
contrastive learning (ICCL) to improve long-tailed representation learning.
ICCL interpolates two images from a class-agnostic sampler and a class-aware
sampler, and trains the model such that the representation of the interpolative
image can be used to retrieve the centroids for both source classes. We
demonstrate the effectiveness of our approach on multiple long-tailed image
classification benchmarks. Our result shows a significant accuracy gain of 2.8%
on the iNaturalist 2018 dataset with a real-world long-tailed distribution.
