Long-tailed recognition with imbalanced class distribution naturally emerges
in practical machine learning applications. Existing methods such as data
reweighing, resampling, and supervised contrastive learning enforce the class
balance with a price of introducing imbalance between instances of head class
and tail class, which may ignore the underlying rich semantic substructures of
the former and exaggerate the biases in the latter. We overcome these drawbacks
by a novel ``subclass-balancing contrastive learning (SBCL)'' approach that
clusters each head class into multiple subclasses of similar sizes as the tail
classes and enforce representations to capture the two-layer class hierarchy
between the original classes and their subclasses. Since the clustering is
conducted in the representation space and updated during the course of
training, the subclass labels preserve the semantic substructures of head
classes. Meanwhile, it does not overemphasize tail class samples, so each
individual instance contribute to the representation learning equally. Hence,
our method achieves both the instance- and subclass-balance, while the original
class labels are also learned through contrastive learning among subclasses
from different classes. We evaluate SBCL over a list of long-tailed benchmark
datasets and it achieves the state-of-the-art performance. In addition, we
present extensive analyses and ablation studies of SBCL to verify its
advantages.

本文提出了一种新的 “子类平衡对比学习（SBCL）” 方法，通过在表示空间中对头类聚类，以捕捉头类和尾类之间的两层类层次结构，实现了实例和子类平衡，并通过在不同类之间的子类对比学习来学习原始类标签。