School of Computer Science and Engineering, South China University of Technology, China.
School of Computer Science and Engineering, South China University of Technology, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, China.
Neural Netw. 2023 Nov;168:214-222. doi: 10.1016/j.neunet.2023.09.022. Epub 2023 Sep 21.
Real-world data often exhibits a long-tailed distribution, in which head classes occupy most of the data, while tail classes only have very few samples. Models trained on long-tailed datasets have poor adaptability to tail classes and the decision boundaries are ambiguous. Therefore, in this paper, we propose a simple yet effective model, named Dual-Branch Long-Tailed Recognition (DB-LTR), which includes an imbalanced learning branch and a Contrastive Learning Branch (CoLB). The imbalanced learning branch, which consists of a shared backbone and a linear classifier, leverages common imbalanced learning approaches to tackle the data imbalance issue. In CoLB, we learn a prototype for each tail class, and calculate an inter-branch contrastive loss, an intra-branch contrastive loss and a metric loss. CoLB can improve the capability of the model in adapting to tail classes and assist the imbalanced learning branch to learn a well-represented feature space and discriminative decision boundary. Extensive experiments on three long-tailed benchmark datasets, i.e., CIFAR100-LT, ImageNet-LT and Places-LT, show that our DB-LTR is competitive and superior to the comparative methods.
真实世界的数据通常呈现长尾分布，其中头部类占据了大部分数据，而尾部类只有很少的样本。在长尾数据集上训练的模型对尾部类的适应能力较差，决策边界也不明确。因此，在本文中，我们提出了一种简单而有效的模型，名为 Dual-Branch Long-Tailed Recognition（DB-LTR），它包括一个不平衡学习分支和一个对比学习分支（CoLB）。不平衡学习分支由一个共享主干和一个线性分类器组成，利用常见的不平衡学习方法来解决数据不平衡问题。在 CoLB 中，我们为每个尾部类学习一个原型，并计算一个分支间对比损失、一个分支内对比损失和一个度量损失。CoLB 可以提高模型适应尾部类的能力，并协助不平衡学习分支学习一个具有代表性的特征空间和判别决策边界。在三个长尾基准数据集 CIFAR100-LT、ImageNet-LT 和 Places-LT 上进行的广泛实验表明，我们的 DB-LTR 具有竞争力，优于比较方法。
