  Significant progress has been made in learning image classification neural
networks under long-tail data distribution using robust training algorithms
such as data re-sampling, re-weighting, and margin adjustment. Those methods,
however, ignore the impact of data imbalance on feature normalization. The
dominance of majority classes (head classes) in estimating statistics and
affine parameters causes internal covariate shifts within less-frequent
categories to be overlooked. To alleviate this challenge, we propose a compound
batch normalization method based on a Gaussian mixture. It can model the
feature space more comprehensively and reduce the dominance of head classes. In
addition, a moving average-based expectation maximization (EM) algorithm is
employed to estimate the statistical parameters of multiple Gaussian
distributions. However, the EM algorithm is sensitive to initialization and can
easily become stuck in local minima where the multiple Gaussian components
continue to focus on majority classes. To tackle this issue, we developed a
dual-path learning framework that employs class-aware split feature
normalization to diversify the estimated Gaussian distributions, allowing the
Gaussian components to fit with training samples of less-frequent classes more
comprehensively. Extensive experiments on commonly used datasets demonstrated
that the proposed method outperforms existing methods on long-tailed image
classification.
