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 this https URL

该论文提出了一种名为 GLMC 的训练策略，旨在解决长尾视觉识别中的特征提取器鲁棒性和分类器偏差问题，其中核心思想包括全局和局部混合一致性损失、累积头尾软标签权重损失等，该方法在多个数据集上达到了最新的准确度，并证明了其对提高邻近算法泛化的有效性。