Real-world data is extremely imbalanced and presents a long-tailed
distribution, resulting in models that are biased towards classes with
sufficient samples and perform poorly on rare classes. Recent methods propose
to rebalance classes but they undertake the seesaw dilemma (what is increasing
performance on tail classes may decrease that of head classes, and vice versa).
In this paper, we argue that the seesaw dilemma is derived from gradient
imbalance of different classes, in which gradients of inappropriate classes are
set to important for updating, thus are prone to overcompensation or
undercompensation on tail classes. To achieve ideal compensation, we formulate
the long-tailed recognition as an multi-objective optimization problem, which
fairly respects the contributions of head and tail classes simultaneously. For
efficiency, we propose a Gradient-Balancing Grouping (GBG) strategy to gather
the classes with similar gradient directions, thus approximately make every
update under a Pareto descent direction. Our GBG method drives classes with
similar gradient directions to form more representative gradient and provide
ideal compensation to the tail classes. Moreover, We conduct extensive
experiments on commonly used benchmarks in long-tailed learning and demonstrate
the superiority of our method over existing SOTA methods.

通过多目标优化，我们提出一种梯度平衡聚类（GBG）策略，以应对现实世界数据中的类别不平衡和长尾分布问题，并在长尾学习中展现出比现有方法更优越的性能。