  It is not uncommon that real-world data are distributed with a long tail. For
such data, the learning of deep neural networks becomes challenging because it
is hard to classify tail classes correctly. In the literature, several existing
methods have addressed this problem by reducing classifier bias, provided that
the features obtained with long-tailed data are representative enough. However,
we find that training directly on long-tailed data leads to uneven embedding
space. That is, the embedding space of head classes severely compresses that of
tail classes, which is not conducive to subsequent classifier learning. This
paper therefore studies the problem of long-tailed visual recognition from the
perspective of feature level. We introduce feature augmentation to balance the
embedding distribution. The features of different classes are perturbed with
varying amplitudes in Gaussian form. Based on these perturbed features, two
novel logit adjustment methods are proposed to improve model performance at a
modest computational overhead. Subsequently, the distorted embedding spaces of
all classes can be calibrated. In such balanced-distributed embedding spaces,
the biased classifier can be eliminated by simply retraining the classifier
with class-balanced sampling data. Extensive experiments conducted on benchmark
datasets demonstrate the superior performance of the proposed method over the
state-of-the-art ones. Source code is available at
https://github.com/Keke921/GCLLoss.
