Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition
Mengke Li Yiu-ming Cheung Yang Lu Zhikai Hu Weichao Lan Hui Huang
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.
%further improving model performance. 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.