  Few-shot learning aims to recognize instances from novel classes with few
labeled samples, which has great value in research and application. Although
there has been a lot of work in this area recently, most of the existing work
is based on image classification tasks. Video-based few-shot action recognition
has not been explored well and remains challenging: 1) the differences of
implementation details among different papers make a fair comparison difficult;
2) the wide variations and misalignment of temporal sequences make the
video-level similarity comparison difficult; 3) the scarcity of labeled data
makes the optimization difficult. To solve these problems, this paper presents
1) a specific setting to evaluate the performance of few-shot action
recognition algorithms; 2) an implicit sequence-alignment algorithm for better
video-level similarity comparison; 3) an advanced loss for few-shot learning to
optimize pair similarity with limited data. Specifically, we propose a novel
few-shot action recognition framework that uses long short-term memory
following 3D convolutional layers for sequence modeling and alignment. Circle
loss is introduced to maximize the within-class similarity and minimize the
between-class similarity flexibly towards a more definite convergence target.
Instead of using random or ambiguous experimental settings, we set a concrete
criterion analogous to the standard image-based few-shot learning setting for
few-shot action recognition evaluation. Extensive experiments on two datasets
demonstrate the effectiveness of our proposed method.
