A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition

• URL: http://arxiv.org/abs/2309.16135v1
• Date: Thu, 28 Sep 2023 03:31:11 GMT
• Title: A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition
• Authors: Qiong Chen, Tianlin Huang, Geren Zhu, Enlu Lin
• Abstract summary: Models trained on long-tailed datasets have poor adaptability to tail classes and the decision boundaries are ambiguous. 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) 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.
• Score: 7.225494453600985
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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