Our work focuses on tackling the challenging but natural visual recognition task of long-tailed data distribution (\ie, a few classes occupy most of the data, while most classes have rarely few samples). In the literature, class re-balancing strategies (\eg, re-weighting and re-sampling) are the prominent and effective methods proposed to alleviate the extreme imbalance for dealing with long-tailed problems. In this paper, we firstly discover that these re-balancing methods achieving satisfactory recognition accuracy owe to that they could significantly promote the classifier learning of deep networks. However, at the same time, they will unexpectedly damage the representative ability of the learned deep features to some extent. Therefore, we propose a unified Bilateral-Branch Network (BBN) to take care of both representation learning and classifier learning simultaneously, where each branch does perform its own duty separately. In particular, our BBN model is further equipped with a novel cumulative learning strategy, which is designed to first learn the universal patterns and then pay attention to the tail data gradually. Extensive experiments on four benchmark datasets, including the large-scale iNaturalist ones, justify that the proposed BBN can significantly outperform state-of-the-art methods. Furthermore, validation experiments can demonstrate both our preliminary discovery and effectiveness of tailored designs in BBN for long-tailed problems. Our method won the first place in the iNaturalist 2019 large scale species classification competition, and our code is open-source and available at https://github.com/Megvii-Nanjing/BBN

提出了针对长尾数据分布的双边分支网络方法，不仅可以同时关注表示学习和分类器学习，还配备了新颖的渐进积累学习策略，并在四种基准数据集上进行了实验，获得了比现有技术更好的性能。

BBN: 双侧分支网络与累积学习用于长尾视觉识别