  Few-shot action recognition aims to recognize action classes with few
training samples. Most existing methods adopt a meta-learning approach with
episodic training. In each episode, the few samples in a meta-training task are
split into support and query sets. The former is used to build a classifier,
which is then evaluated on the latter using a query-centered loss for model
updating. There are however two major limitations: lack of data efficiency due
to the query-centered only loss design and inability to deal with the support
set outlying samples and inter-class distribution overlapping problems. In this
paper, we overcome both limitations by proposing a new Prototype-centered
Attentive Learning (PAL) model composed of two novel components. First, a
prototype-centered contrastive learning loss is introduced to complement the
conventional query-centered learning objective, in order to make full use of
the limited training samples in each episode. Second, PAL further integrates a
hybrid attentive learning mechanism that can minimize the negative impacts of
outliers and promote class separation. Extensive experiments on four standard
few-shot action benchmarks show that our method clearly outperforms previous
state-of-the-art methods, with the improvement particularly significant (10+\%)
on the most challenging fine-grained action recognition benchmark.
