  Deep learning enables impressive performance in image recognition using
large-scale artificially-balanced datasets. However, real-world datasets
exhibit highly class-imbalanced distributions, yielding two main challenges:
relative imbalance amongst the classes and data scarcity for mediumshot or
fewshot classes. In this work, we address the problem of long-tailed
recognition wherein the training set is highly imbalanced and the test set is
kept balanced. Differently from existing paradigms relying on data-resampling,
cost-sensitive learning, online hard example mining, loss objective reshaping,
and/or memory-based modeling, we propose an ensemble of class-balanced experts
that combines the strength of diverse classifiers. Our ensemble of
class-balanced experts reaches results close to state-of-the-art and an
extended ensemble establishes a new state-of-the-art on two benchmarks for
long-tailed recognition. We conduct extensive experiments to analyse the
performance of the ensembles, and discover that in modern large-scale datasets,
relative imbalance is a harder problem than data scarcity. The training and
evaluation code is available at
https://github.com/ssfootball04/class-balanced-experts.
