  Current state-of-the-art approaches for few-shot action recognition achieve
promising performance by conducting frame-level matching on learned visual
features. However, they generally suffer from two limitations: i) the matching
procedure between local frames tends to be inaccurate due to the lack of
guidance to force long-range temporal perception; ii) explicit motion learning
is usually ignored, leading to partial information loss. To address these
issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo)
method that contains two crucial components, including a long-short contrastive
objective and a motion autodecoder. Specifically, the long-short contrastive
objective is to endow local frame features with long-form temporal awareness by
maximizing their agreement with the global token of videos belonging to the
same class. The motion autodecoder is a lightweight architecture to reconstruct
pixel motions from the differential features, which explicitly embeds the
network with motion dynamics. By this means, MoLo can simultaneously learn
long-range temporal context and motion cues for comprehensive few-shot
matching. To demonstrate the effectiveness, we evaluate MoLo on five standard
benchmarks, and the results show that MoLo favorably outperforms recent
advanced methods. The source code is available at
https://github.com/alibaba-mmai-research/MoLo.
