Global and Local Mixture Consistency Cumulative Learning for Long-tailed
  Visual Recognitions
Fei Du Peng Yang Qi Jia Fengtao Nan Xiaoting Chen Yun Yang
In this paper, our goal is to design a simple learning paradigm for long-tail
visual recognition, which not only improves the robustness of the feature
extractor but also alleviates the bias of the classifier towards head classes
while reducing the training skills and overhead. We propose an efficient
one-stage training strategy for long-tailed visual recognition called Global
and Local Mixture Consistency cumulative learning (GLMC). Our core ideas are
twofold: (1) a global and local mixture consistency loss improves the
robustness of the feature extractor. Specifically, we generate two augmented
batches by the global MixUp and local CutMix from the same batch data,
respectively, and then use cosine similarity to minimize the difference. (2) A
cumulative head tail soft label reweighted loss mitigates the head class bias
problem. We use empirical class frequencies to reweight the mixed label of the
head-tail class for long-tailed data and then balance the conventional loss and
the rebalanced loss with a coefficient accumulated by epochs. Our approach
achieves state-of-the-art accuracy on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT
datasets. Additional experiments on balanced ImageNet and CIFAR demonstrate
that GLMC can significantly improve the generalization of backbones. Code is
made publicly available at https://github.com/ynu-yangpeng/GLMC.