Existing methods based on transfer learning leverage auxiliary information to help tail generalization and improve the performance of the tail classes. However, they cannot fully exploit the relationships between auxiliary information and tail classes and bring irrelevant knowledge to the tail classes. To solve this problem, we propose a hierarchical CNN with knowledge complementation, which regards hierarchical relationships as auxiliary information and transfers relevant knowledge to tail classes. First, we integrate semantics and clustering relationships as hierarchical knowledge into the CNN to guide feature learning. Then, we design a complementary strategy to jointly exploit the two types of knowledge, where semantic knowledge acts as a prior dependence and clustering knowledge reduces the negative information caused by excessive semantic dependence (i.e., semantic gaps). In this way, the CNN facilitates the utilization of the two complementary hierarchical relationships and transfers useful knowledge to tail data to improve long-tailed classification accuracy. Experimental results on public benchmarks show that the proposed model outperforms existing methods. In particular, our model improves accuracy by 3.46% compared with the second-best method on the long-tailed tieredImageNet dataset.

现有的基于迁移学习的方法利用辅助信息来帮助尾部泛化并提高尾部类的性能。然而，它们不能充分利用辅助信息和尾类之间的关系，并且给尾类带来不相关的知识。为了解决这个问题，我们提出了一种具有知识互补的分层CNN，它将分层关系视为辅助信息，并将相关知识转移到尾类。首先，我们将语义和聚类关系作为层次知识集成到 CNN 中来指导特征学习。然后，我们设计了一种互补策略来共同利用两种类型的知识，其中语义知识充当先验依赖，而聚类知识减少了过度语义依赖（即语义差距）造成的负面信息。通过这种方式，CNN有利于利用两种互补的层次关系，并将有用的知识转移到尾部数据中，以提高长尾分类的准确性。公共基准的实验结果表明，所提出的模型优于现有方法。特别是，与长尾 tieredImageNet 数据集上的第二佳方法相比，我们的模型将准确率提高了 3.46%。