  Deep neural networks often degrade significantly when training data suffer
from class imbalance problems. Existing approaches, e.g., re-sampling and
re-weighting, commonly address this issue by rearranging the label distribution
of training data to train the networks fitting well to the implicit balanced
label distribution. However, most of them hinder the representative ability of
learned features due to insufficient use of intra/inter-sample information of
training data. To address this issue, we propose meta feature modulator (MFM),
a meta-learning framework to model the difference between the long-tailed
training data and the balanced meta data from the perspective of representation
learning. Concretely, we employ learnable hyper-parameters (dubbed modulation
parameters) to adaptively scale and shift the intermediate features of
classification networks, and the modulation parameters are optimized together
with the classification network parameters guided by a small amount of balanced
meta data. We further design a modulator network to guide the generation of the
modulation parameters, and such a meta-learner can be readily adapted to train
the classification network on other long-tailed datasets. Extensive experiments
on benchmark vision datasets substantiate the superiority of our approach on
long-tailed recognition tasks beyond other state-of-the-art methods.
