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。

全局和局部混合一致性累积学习用于长尾视觉识别

Global and Local Mixture Consistency Cumulative Learning for Long-tailed  Visual Recognitions

The availability of large-scale image captioning and visual question
answering datasets has contributed significantly to recent successes in
vision-and-language pre-training. However, these datasets are often collected
with overrestrictive requirements inherited from their original target tasks
(e.g., image caption generation), which limit the resulting dataset scale and
diversity. We take a step further in pushing the limits of vision-and-language
pre-training data by relaxing the data collection pipeline used in Conceptual
Captions 3M (CC3M) [Sharma et al. 2018] and introduce the Conceptual 12M
(CC12M), a dataset with 12 million image-text pairs specifically meant to be
used for vision-and-language pre-training. We perform an analysis of this
dataset and benchmark its effectiveness against CC3M on multiple downstream
tasks with an emphasis on long-tail visual recognition. Our results clearly
illustrate the benefit of scaling up pre-training data for vision-and-language
tasks, as indicated by the new state-of-the-art results on both the nocaps and
Conceptual Captions benchmarks.

通过松弛 Conceptual Captions 3M (CC3M) [Sharma et al. 2018] 数据收集流程，我们引入了 Conceptual 12M（CC12M）数据集，并通过针对长尾视觉识别的多个下游任务基准测试其有效性，结果表明增加预训练数据规模会使视觉和语言任务更加有效。