  Recently, few-shot action recognition has significantly progressed by
learning the feature discriminability and designing suitable comparison
methods. Still, there are the following restrictions. (a) Previous works are
mainly based on visual mono-modal. Although some multi-modal works use labels
as supplementary to construct prototypes of support videos, they can not use
this information for query videos. The labels are not used efficiently. (b)
Most of the works ignore the motion feature of video, although the motion
features are essential for distinguishing. We proposed a Consistency Prototype
and Motion Compensation Network(CLIP-CP$M^2$C) to address these issues.
Firstly, we use the CLIP for multi-modal few-shot action recognition with the
text-image comparison for domain adaption. Secondly, in order to make the
amount of information between the prototype and the query more similar, we
propose a novel method to compensate for the text(prompt) information of query
videos when text(prompt) does not exist, which depends on a Consistency Loss.
Thirdly, we use the differential features of the adjacent frames in two
directions as the motion features, which explicitly embeds the network with
motion dynamics. We also apply the Consistency Loss to the motion features.
Extensive experiments on standard benchmark datasets demonstrate that the
proposed method can compete with state-of-the-art results. Our code is
available at the URL: https://github.com/xxx/xxx.git.
