There is a growing interest in learning a model which could recognize novel
classes with only a few labeled examples. In this paper, we propose Temporal
Alignment Module (TAM), a novel few-shot learning framework that can learn to
classify a previous unseen video. While most previous works neglect long-term
temporal ordering information, our proposed model explicitly leverages the
temporal ordering information in video data through temporal alignment. This
leads to strong data-efficiency for few-shot learning. In concrete, TAM
calculates the distance value of query video with respect to novel class
proxies by averaging the per frame distances along its alignment path. We
introduce continuous relaxation to TAM so the model can be learned in an
end-to-end fashion to directly optimize the few-shot learning objective. We
evaluate TAM on two challenging real-world datasets, Kinetics and
Something-Something-V2, and show that our model leads to significant
improvement of few-shot video classification over a wide range of competitive
baselines.

本文提出了一种新的少样本学习框架 ——“Temporal Alignment Module (TAM)”，通过显式利用视频数据中的时序信息，计算查询视频与新类别代理之间的距离值以实现视频分类，并引入连续松弛以在端到端的学习中直接优化少样本学习目标，最后，在两个真实世界数据集上的评估表明，模型相比各种基线算法在少样本视频分类领域具有明显的提高。