  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.
