The existing few-shot video classification methods often employ a
meta-learning paradigm by designing customized temporal alignment module for
similarity calculation. While significant progress has been made, these methods
fail to focus on learning effective representations, and heavily rely on the
ImageNet pre-training, which might be unreasonable for the few-shot recognition
setting due to semantics overlap. In this paper, we aim to present an in-depth
study on few-shot video classification by making three contributions. First, we
perform a consistent comparative study on the existing metric-based methods to
figure out their limitations in representation learning. Accordingly, we
propose a simple classifier-based baseline without any temporal alignment that
surprisingly outperforms the state-of-the-art meta-learning based methods.
Second, we discover that there is a high correlation between the novel action
class and the ImageNet object class, which is problematic in the few-shot
recognition setting. Our results show that the performance of training from
scratch drops significantly, which implies that the existing benchmarks cannot
provide enough base data. Finally, we present a new benchmark with more base
data to facilitate future few-shot video classification without pre-training.
The code will be made available at this https URL

本文旨在深入研究几种少量数据学习框架用于视频分类的方法，通过提出基于分类器的基线模型等贡献，发现现有的度量学习法存在的局限性，并发现新行动类别和 ImageNet 物体类别之间高度相关，最终提出了一个新的基准数据集以促进未来的少样本数据的视频分类研究，该代码将在指定网址上公开。

Few-Shot 视频分类的深入研究：新基准和基础线

A Closer Look at Few-Shot Video Classification: A New Baseline and  Benchmark

Many recent advances in computer vision are the result of a healthy
competition among researchers on high quality, task-specific, benchmarks. After
a decade of active research, zero-shot learning (ZSL) models accuracy on the
Imagenet benchmark remains far too low to be considered for practical object
recognition applications. In this paper, we argue that the main reason behind
this apparent lack of progress is the poor quality of this benchmark. We
highlight major structural flaws of the current benchmark and analyze different
factors impacting the accuracy of ZSL models. We show that the actual
classification accuracy of existing ZSL models is significantly higher than was
previously thought as we account for these flaws. We then introduce the notion
of structural bias specific to ZSL datasets. We discuss how the presence of
this new form of bias allows for a trivial solution to the standard benchmark
and conclude on the need for a new benchmark. We then detail the semi-automated
construction of a new benchmark to address these flaws.

本文主要探讨了当前零样本学习（ZSL）模型在 Imagenet 基准测试中的不足之处，提出了基准测试质量不高的原因，并分析了影响 ZSL 模型准确性的不同因素。进一步介绍了 ZSL 数据集中的结构偏见，并详细阐述了半自动构建新基准的方法，以应对这些缺陷。