  Few-shot action recognition aims to address the high cost and impracticality
of manually labeling complex and variable video data in action recognition. It
requires accurately classifying human actions in videos using only a few
labeled examples per class. Compared to few-shot learning in image scenarios,
few-shot action recognition is more challenging due to the intrinsic complexity
of video data. Recognizing actions involves modeling intricate temporal
sequences and extracting rich semantic information, which goes beyond mere
human and object identification in each frame. Furthermore, the issue of
intra-class variance becomes particularly pronounced with limited video
samples, complicating the learning of representative features for novel action
categories. To overcome these challenges, numerous approaches have driven
significant advancements in few-shot action recognition, which underscores the
need for a comprehensive survey. Unlike early surveys that focus on few-shot
image or text classification, we deeply consider the unique challenges of
few-shot action recognition. In this survey, we review a wide variety of recent
methods and summarize the general framework. Additionally, the survey presents
the commonly used benchmarks and discusses relevant advanced topics and
promising future directions. We hope this survey can serve as a valuable
resource for researchers, offering essential guidance to newcomers and
stimulating seasoned researchers with fresh insights.
