  A primary challenge faced in few-shot action recognition is inadequate video
data for training. To address this issue, current methods in this field mainly
focus on devising algorithms at the feature level while little attention is
paid to processing input video data. Moreover, existing frame sampling
strategies may omit critical action information in temporal and spatial
dimensions, which further impacts video utilization efficiency. In this paper,
we propose a novel video frame sampler for few-shot action recognition to
address this issue, where task-specific spatial-temporal frame sampling is
achieved via a temporal selector (TS) and a spatial amplifier (SA).
Specifically, our sampler first scans the whole video at a small computational
cost to obtain a global perception of video frames. The TS plays its role in
selecting top-T frames that contribute most significantly and subsequently. The
SA emphasizes the discriminative information of each frame by amplifying
critical regions with the guidance of saliency maps. We further adopt
task-adaptive learning to dynamically adjust the sampling strategy according to
the episode task at hand. Both the implementations of TS and SA are
differentiable for end-to-end optimization, facilitating seamless integration
of our proposed sampler with most few-shot action recognition methods.
Extensive experiments show a significant boost in the performances on various
benchmarks including long-term videos.The code is available at
https://github.com/R00Kie-Liu/Sampler
