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。

长尾学习作为多目标优化

Long-Tailed Learning as Multi-Objective Optimization

Label distributions in real-world are oftentimes long-tailed and imbalanced,
resulting in biased models towards dominant labels. While long-tailed
recognition has been extensively studied for image classification tasks,
limited effort has been made for video domain. In this paper, we introduce
VideoLT, a large-scale long-tailed video recognition dataset, as a step toward
real-world video recognition. Our VideoLT contains 256,218 untrimmed videos,
annotated into 1,004 classes with a long-tailed distribution. Through extensive
studies, we demonstrate that state-of-the-art methods used for long-tailed
image recognition do not perform well in the video domain due to the additional
temporal dimension in video data. This motivates us to propose FrameStack, a
simple yet effective method for long-tailed video recognition task. In
particular, FrameStack performs sampling at the frame-level in order to balance
class distributions, and the sampling ratio is dynamically determined using
knowledge derived from the network during training. Experimental results
demonstrate that FrameStack can improve classification performance without
sacrificing overall accuracy. Code and dataset are available at:
this https URL

介绍了 VideoLT 数据集，通过 FrameStack 方法对视频中的长尾现象进行处理，通过实验证明该方法可以提高分类性能，而不会牺牲整体准确率。