Recent few-shot action recognition (FSAR) methods achieve promising
performance by performing semantic matching on learned discriminative features.
However, most FSAR methods focus on single-scale (e.g., frame-level,
segment-level, \etc) feature alignment, which ignores that human actions with
the same semantic may appear at different velocities. To this end, we develop a
novel Multi-Velocity Progressive-alignment (MVP-Shot) framework to
progressively learn and align semantic-related action features at
multi-velocity levels. Concretely, a Multi-Velocity Feature Alignment (MVFA)
module is designed to measure the similarity between features from support and
query videos with different velocity scales and then merge all similarity
scores in a residual fashion. To avoid the multiple velocity features deviating
from the underlying motion semantic, our proposed Progressive Semantic-Tailored
Interaction (PSTI) module injects velocity-tailored text information into the
video feature via feature interaction on channel and temporal domains at
different velocities. The above two modules compensate for each other to
predict query categories more accurately under the few-shot settings.
Experimental results show our method outperforms current state-of-the-art
methods on multiple standard few-shot benchmarks (i.e., HMDB51, UCF101,
Kinetics, and SSv2-small).

通过提出一种多速度渐进对齐的方法，在多个常见的少样本动作识别基准测试上，我们的方法在精度上超越了现有的最先进方法。