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.

翻译：现实世界的数据极度不平衡且呈现长尾分布，导致模型偏向于样本充足的类别，而在稀有类别上表现欠佳。现有方法虽尝试重新平衡各类别，却面临跷跷板困境（即提升尾部类别性能可能降低头部类别性能，反之亦然）。本文指出，跷跷板困境源于不同类别梯度分配失衡——不恰当类别的梯度被赋予过高更新权重，导致对尾部类别的过度补偿或补偿不足。为实现理想补偿，我们创新性地将长尾识别建模为多目标优化问题，同时公平考量头部与尾部类别的贡献。为提升效率，提出梯度平衡分组（GBG）策略，将梯度方向相近的类别聚合，使每次参数更新近似沿帕累托下降方向进行。该方法通过驱动梯度方向相似的类别形成更具代表性的梯度，为尾部类别提供理想补偿。我们在长尾学习经典基准数据集上开展大量实验，验证了本方法相较现有最优方法的显著优越性。