  Few-shot video classification aims to learn new video categories with only a
few labeled examples, alleviating the burden of costly annotation in real-world
applications. However, it is particularly challenging to learn a
class-invariant spatial-temporal representation in such a setting. To address
this, we propose a novel matching-based few-shot learning strategy for video
sequences in this work. Our main idea is to introduce an implicit temporal
alignment for a video pair, capable of estimating the similarity between them
in an accurate and robust manner. Moreover, we design an effective context
encoding module to incorporate spatial and feature channel context, resulting
in better modeling of intra-class variations. To train our model, we develop a
multi-task loss for learning video matching, leading to video features with
better generalization. Extensive experimental results on two challenging
benchmarks, show that our method outperforms the prior arts with a sizable
margin on SomethingSomething-V2 and competitive results on Kinetics.
