  Real-world imagery is often characterized by a significant imbalance of the
number of images per class, leading to long-tailed distributions. An effective
and simple approach to long-tailed visual recognition is to learn feature
representations and a classifier separately, with instance and class-balanced
sampling, respectively. In this work, we introduce a new framework, by making
the key observation that a feature representation learned with instance
sampling is far from optimal in a long-tailed setting. Our main contribution is
a new training method, referred to as Class-Balanced Distillation (CBD), that
leverages knowledge distillation to enhance feature representations. CBD allows
the feature representation to evolve in the second training stage, guided by
the teacher learned in the first stage. The second stage uses class-balanced
sampling, in order to focus on under-represented classes. This framework can
naturally accommodate the usage of multiple teachers, unlocking the information
from an ensemble of models to enhance recognition capabilities. Our experiments
show that the proposed technique consistently outperforms the state of the art
on long-tailed recognition benchmarks such as ImageNet-LT, iNaturalist17 and
iNaturalist18. The experiments also show that our method does not sacrifice the
accuracy of head classes to improve the performance of tail classes, unlike
most existing work.
