This paper presents an investigation into long-tail video recognition. We
demonstrate that, unlike naturally-collected video datasets and existing
long-tail image benchmarks, current video benchmarks fall short on multiple
long-tailed properties. Most critically, they lack few-shot classes in their
tails. In response, we propose new video benchmarks that better assess
long-tail recognition, by sampling subsets from two datasets: SSv2 and VideoLT.
We then propose a method, Long-Tail Mixed Reconstruction, which reduces
overfitting to instances from few-shot classes by reconstructing them as
weighted combinations of samples from head classes. LMR then employs label
mixing to learn robust decision boundaries. It achieves state-of-the-art
average class accuracy on EPIC-KITCHENS and the proposed SSv2-LT and
VideoLT-LT. Benchmarks and code at: tobyperrett.github.io/lmr

本文对长尾视频识别进行了研究，提出了新的视频基准数据集和一种名为 Long-Tail Mixed Reconstruction 的方法，可显著减少过拟合并取得了最新的平均分类精度。

运用头脑：提升长尾视频识别

Use Your Head: Improving Long-Tail Video Recognition

The problem of long-tailed recognition, where the number of examples per
class is highly unbalanced, is considered. It is hypothesized that the well
known tendency of standard classifier training to overfit to popular classes
can be exploited for effective transfer learning. Rather than eliminating this
overfitting, e.g. by adopting popular class-balanced sampling methods, the
learning algorithm should instead leverage this overfitting to transfer
geometric information from popular to low-shot classes. A new classifier
architecture, GistNet, is proposed to support this goal, using constellations
of classifier parameters to encode the class geometry. A new learning algorithm
is then proposed for GeometrIc Structure Transfer (GIST), with resort to a
combination of loss functions that combine class-balanced and random sampling
to guarantee that, while overfitting to the popular classes is restricted to
geometric parameters, it is leveraged to transfer class geometry from popular
to few-shot classes. This enables better generalization for few-shot classes
without the need for the manual specification of class weights, or even the
explicit grouping of classes into different types. Experiments on two popular
long-tailed recognition datasets show that GistNet outperforms existing
solutions to this problem.

提出了一种新的分类器体系结构 GistNet，并设计了一种新的学习算法 GeometrIc Structure Transfer（GIST），该算法使用多种损失函数，利用过度拟合来从受欢迎的类向少量样本的类转移几何信息，从而实现了更好的少量样本类别的泛化，无需手动指定 class weights 或将类别明确分组。在两个流行的长尾识别数据集上，GistNet 比现有解决方案表现更好。