  Semi-supervised video action recognition tends to enable deep neural networks
to achieve remarkable performance even with very limited labeled data. However,
existing methods are mainly transferred from current image-based methods (e.g.,
FixMatch). Without specifically utilizing the temporal dynamics and inherent
multimodal attributes, their results could be suboptimal. To better leverage
the encoded temporal information in videos, we introduce temporal gradient as
an additional modality for more attentive feature extraction in this paper. To
be specific, our method explicitly distills the fine-grained motion
representations from temporal gradient (TG) and imposes consistency across
different modalities (i.e., RGB and TG). The performance of semi-supervised
action recognition is significantly improved without additional computation or
parameters during inference. Our method achieves the state-of-the-art
performance on three video action recognition benchmarks (i.e., Kinetics-400,
UCF-101, and HMDB-51) under several typical semi-supervised settings (i.e.,
different ratios of labeled data).
