In vision domain, large-scale natural datasets typically exhibit long-tailed distribution which has large class imbalance between head and tail classes. This distribution poses difficulty in learning good representations for tail classes. Recent developments have shown good long-tailed model can be learnt by decoupling the training into representation learning and classifier balancing. However, these works pay insufficient consideration on the long-tailed effect on representation learning. In this work, we propose interpolative centroid contrastive learning (ICCL) to improve long-tailed representation learning. ICCL interpolates two images from a class-agnostic sampler and a class-aware sampler, and trains the model such that the representation of the interpolative image can be used to retrieve the centroids for both source classes. We demonstrate the effectiveness of our approach on multiple long-tailed image classification benchmarks. Our result shows a significant accuracy gain of 2.8% on the iNaturalist 2018 dataset with a real-world long-tailed distribution.
翻译:在视觉领域,大型自然数据集通常会显示长尾类和尾类之间大类不平衡的长尾类分布。这种分布在学习尾类的良好表现方面造成困难。最近的事态发展表明,通过将培训与代表性学习和分类平衡脱钩,可以学习良好的长尾型模型。然而,这些工作对于代表学习的长期尾目影响没有给予足够的考虑。在这项工作中,我们提议采用中间偏差的对称学习(ICCL)来改进长期代表性学习。 ICCL将两个来自类、无脊椎取样员和有阶级抽样取样员的图像相互调试,并培训模型,以便利用中间图象来检索两种源类的中间图象。我们展示了我们在多种长尾目图像分类基准方面的做法的有效性。我们的结果显示,在2018年的i-Natalist数据集中,以真实的长尾分布方式取得了2.8%的精度收益。