  The goal of few-shot video classification is to learn a classification model
with good generalization ability when trained with only a few labeled videos.
However, it is difficult to learn discriminative feature representations for
videos in such a setting. In this paper, we propose Temporal Alignment
Prediction (TAP) based on sequence similarity learning for few-shot video
classification. In order to obtain the similarity of a pair of videos, we
predict the alignment scores between all pairs of temporal positions in the two
videos with the temporal alignment prediction function. Besides, the inputs to
this function are also equipped with the context information in the temporal
domain. We evaluate TAP on two video classification benchmarks including
Kinetics and Something-Something V2. The experimental results verify the
effectiveness of TAP and show its superiority over state-of-the-art methods.
