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。