The imbalanced distribution of long-tailed data poses a challenge for deep neural networks, as models tend to prioritize correctly classifying head classes over others so that perform poorly on tail classes. The lack of semantics for tail classes is one of the key factors contributing to their low recognition accuracy. To rectify this issue, we propose to augment tail classes by borrowing the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T). We randomly replace a portion of the feature maps of the tail class with those of the head class. The fused feature map can effectively enhance the diversity of tail classes by incorporating features from head classes that are relevant to them. The proposed method is easy to implement due to its additive fusion module, making it highly compatible with existing long-tail recognition methods for further performance boosting. Extensive experiments on various long-tailed benchmarks demonstrate the effectiveness of the proposed H2T. The source code is temporarily available at https://github.com/Keke921/H2T.

翻译：长尾数据的不平衡分布对深度神经网络构成挑战，因为模型倾向于优先正确分类头部类别而忽视其他类别，导致尾部类别的识别性能较差。尾部类别语义信息的匮乏是导致其识别准确率低下的关键因素之一。为解决此问题，我们提出通过借用头部类别的多样语义信息来增强尾部类别，称之为从头至尾融合（H2T）。我们随机将尾部类别的部分特征图替换为头部类别的对应特征图。这种融合特征图通过融入与尾部类别相关的头部类别特征，能有效增强尾部类别的多样性。所提方法因其加法融合模块而易实现，具有良好的兼容性，可进一步提升现有长尾识别方法的性能。在多种长尾基准数据集上的大量实验验证了所提H2T方法的有效性。源代码临时发布于https://github.com/Keke921/H2T。