  High frame-rate (HFR) videos of action recognition improve fine-grained
expression while reducing the spatio-temporal relation and motion information
density. Thus, large amounts of video samples are continuously required for
traditional data-driven training. However, samples are not always sufficient in
real-world scenarios, promoting few-shot action recognition (FSAR) research. We
observe that most recent FSAR works build spatio-temporal relation of video
samples via temporal alignment after spatial feature extraction, cutting apart
spatial and temporal features within samples. They also capture motion
information via narrow perspectives between adjacent frames without considering
density, leading to insufficient motion information capturing. Therefore, we
propose a novel plug-and-play architecture for FSAR called Spatio-tempOral
frAme tuPle enhancer (SOAP) in this paper. The model we designed with such
architecture refers to SOAP-Net. Temporal connections between different feature
channels and spatio-temporal relation of features are considered instead of
simple feature extraction. Comprehensive motion information is also captured,
using frame tuples with multiple frames containing more motion information than
adjacent frames. Combining frame tuples of diverse frame counts further
provides a broader perspective. SOAP-Net achieves new state-of-the-art
performance across well-known benchmarks such as SthSthV2, Kinetics, UCF101,
and HMDB51. Extensive empirical evaluations underscore the competitiveness,
pluggability, generalization, and robustness of SOAP. The code is released at
https://github.com/wenbohuang1002/SOAP.
